IEEE WCNC 2020

Session OPNTU

Opening Session on Tuesday

Conference

9:00 AM — 9:20 AM KST

Local

May 25 Mon, 8:00 PM — 8:20 PM EDT

Sponsor Commercial/Welcome

23

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Opening

Saewoong Bahk, WCNC 2020 General Chair

13

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Welcome message

Halim Yanikomeroglu, WCNC Steering Committee Chair

10

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TPC Report & Best paper award

Song Chong, WCNC 2020 TPC Chair

12

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Session Chair

Saewoong Bahk (Seoul National University, Korea (South))

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Session KNTU-S1

Keynote

Conference

9:20 AM — 10:00 AM KST

Local

May 25 Mon, 8:20 PM — 9:00 PM EDT

Next Steps and Challenges of 5G Network Evolution

Young Lee, Head of Network Architecture, Samsung

23

On April 2019, the world-first 5G network was launched commercially in South Korea. The early stage of 5G roll-out was targeted only to enhanced Mobile BroadBand (eMBB) service, and its control depended on LTE as an anchor. The real 5G will come with more enhanced features to support higher capacity, Integrated Access & Backhaul (IAB), Ultra-Reliable and Low Latency Communications (URLLC), Industrial IoT, Vehicle-to-everything (V2X), and AR/VR. 5G Stand-alone (SA) deployment is a first step to show full potential of 5G as 5G SA brings the foundation for end-to-end (E2E) service pipeline across different domains –Radio Access Network (RAN), Core Network (CN), Transport Network (TN), and Data Network (DN) – to accelerate innovations of Mobile Network Operators (MNO). While innovations are important, network monetization and Operating Expenditure (OPEX)/ Capital Expenditure (CAPEX) reduction are key concerns of Mobile Network Operators (MNOs) to justify huge investment costs. In this regard, the benefits from virtualization, network slicing and automation are driving forces for 5G network evolution. This talk explores the technological challenges from trending areas such as virtualized RAN, E2E network slicing, and network automation. In addition, this talk presents a view on the central role of virtualization and cloudification towards technological innovation and cost reduction.

Biography

Young Lee has been in telecommunication industry over 30 years. His specialty includes network architecture, SDN/NFV, network orchestration, 5G transport and core network design, network control and management. He is currently Head of Network Architecture at Samsung Electronics Networks Business where he is leading network architecture evolution and strategy to transform various elements such as RAN, Core, Transport with AI/NFV/SDN and Orchestration into integrated solutions. At Huawei US Research center in Texas USA (2006-2019) he was Technical Senior Director and Distinguished Engineer and led several key technology concept developments, standardization and evangelization in the areas of optical control plane, path computation, transport SDN, network cloud platform and orchestration. At Ceterus Network (2001-2006), he was Co-Founder and Chief Network Architect and led a large-scale packet switching system development. At AT&T Labs (1995-2000), He was Principal Member of Technical Staff where he led various systems engineering projects including AT&T next generation router evolution, AT&T common IP/MPLS backbone routing and management, etc. At AT&T Bell Labs (1987-1995), he was Member of Technical Staff and led several routing and switching system engineering projects and network traffic management system development. He received B.A. in Applied Mathematics from U.C. Berkeley (1986), M.S. in Operations Research from Stanford University (1987), and Ph.D. in Decision Science and Engineering Systems from Rensselaer Polytechnic Institute (1996) via AT&T Bell Labs’ doctoral support program.

Session Chair

Sunghyun Choi (Samsung Electronics, Korea (South))

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Session KNTU-S2

Keynote

Conference

10:00 AM — 10:40 AM KST

Local

May 25 Mon, 9:00 PM — 9:40 PM EDT

Challenges and Opportunities of 5G Mobile Edge Cloud

Dr. Kang-Won Lee (SK Telecom)

17

The 5G services require a network with “high bandwidth and ultra-low latency.” High bandwidth can be enabled by wider frequency bands. To achieve ultra-low latency, however, network operators have come up with the concept of “mobile edge.” By leveraging mobile edge, we can deliver novel 5G applications that can benefit from sub 10msec latency, such as cloud XR, cloud gaming, connected cars, cloud robots.

While providing ultra-low latency itself is useful, this does not fully justify the cost of deployment of numerous edge sites. In fact, it is not difficult to see mobile edge provides a couple of additional benefits: (1) huge volumes of data (that may be generated by, for example, connected cars) can be processed at the edge instead of sending them to a remote data center, which is extremely costly; (2) mission-critical and sensitive data from a smart factory or hospital can be processed at the edge without leaving the site. By enabling edge data processing and local security, mobile edge provides a unique opportunity for mobile service providers to bring new values to its B2C and B2B customers.

In this keynote, I propose that mobile edge should be “programmable” and “cloud native.” This does not mean just running a few VMs at the edge site. At SKT we are developing its mobile edge as a fully functioning cloud. SKT’s MEC or “mobile edge cloud” will provide virtualized infrastructure with Kubernetes, serverless, and service mesh support. We are also pairing our MEC with public clouds so that our users have options to quickly build new applications using widely understood cloud APIs and services. In addition, we will provide our unique service assets, such as telco APIs, natural language processing, real-time data processing, etc. “as a service” to developers so they can quickly build something that was truly not possible before.

I will conclude this talk by presenting several early use cases that we are developing on 5G MEC with our partners.

Session Chair

Sunghyun Choi (Samsung Electronics, Korea (South))

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Session T1-S1

Millimeter-Wave Systems 1

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

An Analytical Model for Efficient Indoor THz Access Point Deployment

Rohit Singh and Doug Sicker (Carnegie Mellon University, USA)

3

Ultra-densification of user equipment (UE) and access points (APs) are anticipated to take a toll on the future spectrum needs. Higher frequency bands, such as mmWave (30-300GHz) and THz spectrum (0.3-10THz), can be used to cater to the high-throughput needs of ultra-dense networks. These high-frequency bands have a tremendous amount of green-filed contiguous spectrum, ranging in hundreds of GHz. However, these bands, especially the THz bands, face numerous challenges, such as high spreading, absorption, and penetration losses. To combat these challenges, the THz-APs need to be either equipped with high transmit power, high antenna gains (i.e., narrow antenna beams), or limit the communication to short-ranges. All of these factors are bounded due to technical or economic challenges, which will result in a "distance-power dilemma" while deciding on the deployment strategy of THz-APs. In this paper, we present an analytical model to deploy THz-APs in an indoor setting efficiently. We further show through extensive numerical analysis, the optimal number of APs and optimal room length for different blocks of the THz spectrum. Furthermore, these THz-APs need to be efficiently packed to avoid outages due to handoffs, which can add more complexity to the dilemma. To mitigate the packing problem, we propose two solutions over the optimal solution: (a) Radius Increase, and (b) Repeater Assistance, and present an analytical model for each.

Opportunistic Hybrid Beamforming based on Adaptive Perturbation for mmWave Multi-User MIMO Systems

Thuan Van Le and Kyungchun Lee (Seoul National University of Science and Technology, Korea (South))

2

In this paper, we propose an adaptive perturbation- aided opportunistic hybrid beamforming (AP-OHBF) scheme for temporally correlated millimeter wave (mmWave) channels. The basic idea is that the proposed scheme perturbs the channel parameters to track the channels of the scheduled mobile stations (MSs), whose channels are in favorable conditions, based on only the signal-to-interference-plus-noise ratio (SINR) information of MSs. The BS utilizes these parameters to generate the precoders and adopts the successful perturbation for data transmission, which achieves performance improvements. By contrast, if a perturbation results in performance degradation, it is discarded, whereas the precoders corresponding to the best-known parameters in the memory are instead used.

Maximum Sub-array Diversity for mmWave Network under RF Power Leakage and Distortion Noises

Leila Tlebaldiyeva and Behrouz Maham (Nazarbayev University, Kazakhstan); Olav Tirkkonen (Aalto University, Finland)

2

This paper investigates millimeter wave (mmWave) networks operating on a hybrid beamforming (HB) system where a base station with massive MIMO antennas communicates with user equipment (UE) nodes equipped with a single antenna. Space division multiple access allows simultaneous data transmission,however, it causes RF power leakage between UE nodes that originates from side/back lobe gains of antenna arrays. A maximum sub-array transmission diversity technique implemented on HB is proposed to improve the system performance under RF power leakage and residual distortion noise. In this work, we emphasize how RF power leakage and residual distortion noise constraints degrade the quality of communication performance in terms of outage probability (OP) and ergodic capacity. The closed-form expressions for the OP and ergodic capacity are corroborated through Monte-Carlo simulations. Simulation results demonstrate that the effect of distortion noise is more severe at high signal power due to the proportionality of distortion noise to signal power.

Enabling Massive Connections Using Hybrid Beamforming in Terahertz Micro-Scale Networks

Hang Yuan (Beijing Institute of Technology, China); Nan Yang (The Australian National University, Australia); Kai Yang (Beijing Institute of Technology, China); Chong Han (Shanghai Jiao Tong University, China); Jianping An (Beijing Institute of Technology, China)

2

We propose a novel hybrid beamforming (BF) scheme with distance-aware multi-carrier (DAMC) modulation and beam division multiple access (BDMA) to enable massive connections in terahertz (THz) micro-scale networks. This scheme breaks a fundamental limitation in hybrid BF, i.e., the number of users that are simultaneously supported cannot exceed the number of RF chains. Some unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, are exploited in this scheme. First, we propose a user grouping scheme with rough beam pre-scanning and a DAMC spectrum allocation scheme to eliminate intra- group interference. Then, we propose a wideband hybrid BF designing algorithm using the principles of BDMA to control inter-group interference. Furthermore, we propose an iterative power allocation strategy to maximize the achievable sum-rate of the network. Simulation results are presented to show that our proposed hybrid BF DAMC-BDMA scheme achieves higher sumrate than the fully digital BF scheme in the high transmit power regime, due to the high sparsity of THz channels. Simulation results also demonstrate that our iterative power allocation strategy has strong robustness against uncertain interferences.

Particle Swarm Optimization Inspired Low-complexity Beamforming for MmWave Massive MIMO Systems

Lina Hou (Inner Mongolia University, China); Yang Liu (Institute of Electronic Information Engineering, Inner Mongolia University, China); Xuehui Ma and Yuting Li (Inner Mongolia University, China); Shun Na (Institute of Electronic Information Engineering, Inner Mongolia University, China); Minglu Jin (Dalian University of Technology, China)

4

The codebook-based techniques are extensively utilized in analog beamforming and combining to overcome high path-loss in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communications. However, to find the best analog precoder and combiner, a complex search based on predefined codebook is required in conventional schemes, which leads to large time cost. For the purpose of reducing complexity, we propose a new integer coded quantified angles- based particle swarm optimization (IC-PSO) beamforming algorithm. We firstly propose a joint search scheme based on integer coded (IC) quantified angles which transforms the search space into the integer field to simplify the search space. To converge to the optimal solution quickly, an improved particle swarm optimization (PSO) algorithm is further proposed. In this way, the best precoder and combiner can be found with lower complexity. Furthermore, we optimize the inertia weight and acceleration coefficients and process the out-of-bounds particles, which can improve the search ability of the PSO. Theoretical analysis indicates that the proposed IC-PSO beamforming has the lower complexity than some existing methods. Simulation results show that the algorithm has a satisfactory achievable rate which can achieve almost 98% performance of the full-search beamforming.

Session Chair

Seungnyun Kim (Seoul National University, Korea (South))

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Session T1-S2

Deep Learning for Wireless Communications 1

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

Adversarial Jamming Attacks on Deep Reinforcement Learning Based Dynamic Multichannel Access

Chen Zhong, Feng Wang, M. Cenk Gursoy and Senem Velipasalar (Syracuse University, USA)

2

Adversarial attack strategies have been widely studied in machine learning applications, and now are increasingly attracting interest in wireless communications as the application of machine learning methods to wireless systems grows along with security concerns. In this paper, we propose two adversarial policies, one based on feed-forward neural networks (FNNs) and the other based on deep reinforcement learning (DRL) policies. Both attack strategies aim at minimizing the accuracy of a DRL-based dynamic channel access agent. We first present the two frameworks and the dynamic attack procedures of the two adversarial policies. Then we demonstrate and compare their performances. Finally, the advantages and disadvantages of the two frameworks are identified.

Multi-Agent Deep Reinforcement Learning for Secure UAV Communications

Yu Zhang (Tsinghua University, China); Zirui Zhuang (Beijing University of Posts and Telecommunications, China); Feifei Gao (Tsinghua University, China); Jingyu Wang (Beijing University of Posts and Telecommunications, China); Zhu Han (University of Houston, USA)

3

In this paper, we investigate a multi-unmanned aerial vehicle (UAV) cooperation mechanism for secure communications, where the UAV transmitter moves around to serve the multiple ground users (GUs) while the UAV jammers send the 3D jamming signals to the ground eavesdroppers (GEs) to protect the UAV transmitter from being wiretapped. The 3D jamming guarantees the GEs not being interfered by the jamming signals. It is challenging to make a joint trajectory design and power control for a UAV team without central control. To this end, we propose a multi-agent deep reinforcement learning approach to achieve the maximum sum secure rate by designing the dynamic trajectory of each UAV. The proposed multi-agent deep deterministic policy gradient (MADDPG) technique is centralized training at high altitude platforms (HAPs) and distributed execution at each UAV, which enables the fully distributed cooperation among UAVs. Finally, the simulation results show the proposed method can efficiently solve the multi-UAV cooperation trajectory design problem in secure communication scenarios.

Autoencoder based Friendly Jamming

Bui Minh Tuan (University of Engineering and Technology, Vietnam); Duc-Tuyen Ta (LRI, Université Paris Saclay, France & University of Engineering and Technology, VNU-HN, Vietnam); Nguyen Linh Trung (Vietnam National University, Hanoi, Vietnam); Nguyen Viet Ha (VNU Ha Noi, Vietnam)

1

Physical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information (CSI) of their channel. Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to- end learning in AE-based FJ.

Exploiting a low-cost CNN with skip connection for robust automatic modulation classification

Thien Huynh-The (Kumoh National Institute of Technology, Korea (South)); Cam-Hao Hua (Kyung Hee University, Korea (South)); Jae Woo Kim, Seung-Hwan Kim and Dong Seong Kim (Kumoh National Institute of Technology, Korea (South))

4

Recently, deep learning (DL) is an innovative machine learning (ML) technique that has gained the outstanding achievements in computer vision and natural language processing. This work takes advantage of DL for effectively handling automatic modulation classification (AMC), which is the fundamental function of numerous cognitive radio-based and spectrum sensing-based applications in many modern communication systems. Concretely, a novel deep convolutional neural network (DCNN) is proposed for learning a classification model from a massive amount of modulated signals, in which the network architecture has several convolutional blocks specialized to simultaneously capture the temporal intra-signal correlations and the spatial inter-signal relations. To this end, each block comprises various convolutional layers of asymmetric convolution kernels, whose outputs are gathered via a concatenation layer. For the enrichment of multi-scale deep feature and the prevention of gradient vanishing problem, these blocks are associated by skip connections to take into account the useful residual information. In experiments, the proposed CNN-based AMC method achieves the overall 24-modulation classification rate of 88.22% at 10dB SNR on the well-known DeepSig dataset.

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

Alva Kosasih, Wibowo Hardjawana and Branka Vucetic (The University of Sydney, Australia); Chao-Kai Wen (National Sun Yat-sen University, Taiwan)

3

Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multipleinput-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum 2 dB and 19 times for various M-MIMO system configurations.

Session Chair

Wonjae Shin (Pusan National University, Korea (South))

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Session T1-S3

Energy Efficiency in 5G and HetNets

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

Energy Harvesting-Enabled Full-Duplex DF Relay Systems with Improper Gaussian Signaling

Jhe-Yi Lin and Ronald Y. Chang (Academia Sinica, Taiwan); Hen-Wai Tsao and Hsuan-Jung Su (National Taiwan University, Taiwan)

0

Loop interference (LI) is the main performance- limiting factor in full-duplex (FD) relaying systems. In this paper, we propose using improper Gaussian signaling (IGS) in joint source and relay transmission to achieve a better LI resistance and, as a result, a better end-to-end system throughput in energy harvesting (EH) enabled FD relaying systems. An alternating optimization (AO) algorithm is proposed to solve the challenging design problem of finding the optimal transmission with IGS at both the source and relay, as well as the optimal power-splitting (PS) factor at the EH-enabled relay. The relationship between the PS factor and the optimal pseudo-variances is derived in closed form. Numerical results demonstrate the superior throughput performance yielded by IGS at the same LI level, and suggest the possibility of employing IGS as a replacement of a sophisticated LI canceller in practical FD relaying systems.

Low-Complexity Hybrid Precoding and Combining Scheme Based on Array Response Vectors

Eduard Bahingayi and Kyungchun Lee (Seoul National University of Science and Technology, Korea (South))

3

The hybrid precoding and combining algorithms for mmWave massive multiple-input multiple-output (MIMO) systems must consider the trade-off between the complexity and performance of the system. Unfortunately, because of the unit-norm constraint imposed by the use of phase shifters, the optimization of the radio frequency (RF) precoder and combiner becomes a non-convex problem. As a consequence, the algorithm for hybrid precoding and combining design often incurs high complexity. This paper proposes a low-complexity algorithm for hybrid precoding and combining design based on array response vectors. The proposed algorithm considers a decoupled optimization scheme between the RF and baseband domains for the spectral efficiency-maximization problem. In the RF domain, we propose an incremental successive selection method to find a subset of array response vectors from a dictionary, which forms the RF precoding/combining matrices. For the digital domain, we employ singular-value decomposition (SVD) of the low-dimensional effective channel matrix to generate the digital baseband precoder and combiner. Through numerical simulation, we show that the proposed algorithm achieves near- optimal performance with 89.9% — 99.4% complexity reduction compared to the conventional state-of-the-art hybrid precoding and combining algorithm.

Energy Efficiency Optimization for Beamspace Massive MIMO Systems with Low-Resolution ADCs

Hualian Sheng and Xihan Chen (Zhejiang University, China); Kaiming Shen (University of Toronto, Canada); Xiongfei Zhai (Guangdong University of Technology, China); An Liu and Minjian Zhao (Zhejiang University, China)

1

In this article, we propose a sparse hybrid combining (SHC) scheme for the uplink transmission of beamspace massive multiple-input multiple-output (MIMO) system with low- resolution analog to digital converters (LADCs), to alleviate the performance bottleneck caused by the multi-user interference and quantization noise, with reduced hardware cost and power consumption. To this end, we formulate the optimization of the proposed SHC scheme as a system energy efficiency maximization problem under some practical constraints. The resulting problem contains the highly coupled nonconvex objective function, as well as the discrete binary constraints. By exploiting some fractional programming (FP) techniques and introducing auxiliary variables, we first recast the original challenging problem into a more tractable yet equivalent form. We then develop an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) method to find its local stationary solutions. Finally, simulation results verify the effectiveness of the proposed SHC scheme by numerical examples in terms of the achieved system energy efficiency.

Energy-Efficient Design for Massive Access in B5G Cellular Internet of Things

Feiyan Tian and Xiaoming Chen (Zhejiang University, China)

0

In this paper, we investigate an energy-efficient grant-free random access protocol for beyond fifth-generation (B5G) cellular internet of things (IoT) with sporadic traffic. A design framework, including device activity information (DAI) and channel state information (CSI) acquisition and uplink data transmission, is first provided to support massive access over limited radio spectrum. Then, considering the low-power requirement of IoT devices, we propose a robust massive access scheme by jointly optimizing pilot transmit power, data transmit power and receive vector at the base station (BS) for maximizing the energy efficiency in the presence of channel uncertainty. Finally, extensive simulation results validate the effectiveness and robustness of the proposed scheme.

Energy Efficient Ultra-Dense Network Using Long Short-Term Memory

Junwon Son, Seungnyun Kim and Byonghyo Shim (Seoul National University, Korea (South))

0

The energy consumption of cellular systems is becoming a matter of grave concern in both economic and environmental perspectives. Recently, in order to reduce the energy consumption of base stations (BSs), which takes the largest portion, turning off under-loaded BSs has been suggested. However, determining the on/off mode of BSs is a non-convex optimization problem. Also, the problem must be solved in accordance with the time-varying environment since the transition overhead in the future may outrun the power saving at the moment. In this paper, we propose Long Short-Term Memory (LSTM) based framework to make far-sighted control decisions maximizing energy efficiency from a long-term perspective. The LSTM-based network can intelligently determine the on/off modes, utilizing the time-correlated property of the channel and approximating complex mapping between channel state and desired power control coefficient. Lastly, through the convex optimization technique, the optimal power allocation for the active BSs can be found. Simulation results show that the proposed technique outperforms the conventional techniques by a large margin.

Session Chair

Wan Choi (Seoul National University & KAIST, Korea (South))

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Session T2-S1

Multiple Access

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

LiSCAN: Visible Light Uni-Directional Control Channel for Uplink Radio Access

Sharan Naribole (Samsung Semiconductor, Inc., USA); Edward W. Knightly (Rice University, USA)

4

Contention-based uplink radio access might lead to significant degradation in airtime efficiency and energy efficiency as the time spent "awake" by the radio is dependent on the network traffic conditions. In this paper, we design and evaluate LiSCAN, a visible light uni-directional control channel for contention-free uplink radio access. LiSCAN enables a virtual full-duplex operation by broadcasting polling frames (light-polls) across distributed LED luminaires concurrently with data reception over radio. In LiSCAN, each client consists of an additional low-power light sensor, which upon hearing a light-poll directed to it by Access Point (AP), wakes up the radio module only if there is backlogged traffic. To maximize the airtime efficiency, LiSCAN transmits light-polls successively until it detects an uplink radio transmission. LiSCAN's pipelined polling enables clients to detect failure in uplink packet reception and accordingly abort their transmissions to eliminate collisions at the AP. We simulate LiSCAN and alternate strategies in ns- 3 network simulator to analyze LiSCAN's performance under varying traffic conditions. Our results show that LiSCAN can provide significant improvements in the radio airtime efficiency, the sessions delivered with pre-defined service quality requirements and energy savings.

A Joint Angle and Distance based User Pairing Strategy for Millimeter Wave NOMA Networks

Xiaolin Lu and Yong Zhou (ShanghaiTech University, China); Vincent W.S. Wong (University of British Columbia, Canada)

2

In this paper, we consider downlink non-orthogonal multiple access (NOMA) transmission in millimeter wave (mmWave) networks with spatially random users. To facilitate NOMA transmission in mmWave networks, we propose a novel joint angle and distance based user pairing strategy. In particular, the user located nearest to the base station (BS) is paired with another user that is located within a distance threshold from the BS and has the minimum relative spatial angle difference. In consideration of the directional beamforming and the randomness of user locations, the BS opportunistically chooses to enable NOMA or orthogonal multiple access (OMA) based on the instantaneous spatial angle difference between the paired users. The proposed scheme fully exploits the antenna array gain for the paired NOMA users. By using tools from stochastic geometry, we derive the coverage probability of the proposed scheme. Simulations validate the theoretical analysis. Results reveal that the proposed scheme outperforms the angle-based NOMA, distance-based NOMA, and OMA schemes, confirming the importance of exploiting both the angle and distance information for user pairing in mmWave networks. Results also show that there exists an optimal value of the distance threshold that maximizes the coverage probability.

An Adapted RFID Anti-collision Algorithm in a Dynamic Environment

Weiping Zhu and Mingzhe Li (Wuhan University, China); Jiannong Cao (Hong Kong Polytechnic Univ, Hong Kong); Xiaohui Cui (Wuhan University, China)

2

In recent decades, radio frequency identification (RFID) has been used in many applications in the world. Tag anti-collision is a fundamental technique for RFID, for solving the collisions when multiple RFID tags transmit their IDs to an RFID reader simultaneously. This technique is well investigated in the stationary environment, however, has some deficiencies when the number of tags in the interrogation region of the reader changes dramatically. This paper proposes a tag anti-collision algorithm called TAD to solve this problem. TAD can effectively and fast estimate the number of arriving and leaving tags, and automatically adapt to different changes of the tags using a hybrid method. The simulation results show that TAD significantly outperforms existing approaches in the situation with many leaving tags.

A Simple Novel Idle Slot Prediction and Avoidance Scheme Using Prediction Bits for DFSA in RFID

Gan Luan (Beijing University of Posts and Telecommunications, China); Norman C Beaulieu (Beijing University of Posts and Telecommunications BUPT, China)

3

Idle slots cause identification inefficiency in Aloha- based RFID algorithms. An idle slot prediction and elimination technique is proposed. The algorithm is dubbed idle predicting dynamic frame-slotted Aloha (IP-DFSA). In IP-DFSA, the reader reads a slot for RN16 as well as the idle prediction bits. Using only a few prediction bits, IP-DFSA can predict and eliminate a significant number of successive idle slots following the current time slot. Whereas previous schemes only achieve 36% efficiency on average, simulation results show that IP-DFSA with 1, 2, 3, and 4 idle slot prediction bits achieves 45%, 52.5%, 56%, and 60% system efficiencies, with 83%, 86%, 88%, and 89% time efficiencies, respectively. The system efficiencies of IP-DFSA range from 9% higher to 24% higher than previous schemes for 1 prediction bit to 4 prediction bits. The number k of idle prediction bits is optimized, and it is revealed that koptimal =3D 4 for tag numbers n ≥ 6.

ML Estimation and MAP Estimation for Device Activity in Grant-Free Massive Access with Interference

Dongdong Jiang (Shanghai Jiao Tong University, China); Ying Cui (Shanghai Jiaotong University, China)

2

Device activity detection is one main challenge in grant-free random access, which is recently proposed to support massive access for massive machine-type communications (mMTC). Existing solutions fail to consider interference generated by massive Internet of Things (IoT) devices, or important prior information on device activities and interference. In this paper, we consider device activity detection at an access point (AP) in the presence of interference generated by massive devices from other cells. We consider the joint maximum likelihood (ML) estimation and the joint maximum a posterior probability (MAP) estimation of both the device activities and interference powers, jointly utilizing tools from probability, stochastic geometry and optimization. Each estimation problem is a difference of convex (DC) programming problem, and a coordinate descent algorithm is proposed to obtain a stationary point. The proposed ML estimation extends the existing ML estimation by considering the estimation of interference powers together with the estimation of device activities. The proposed MAP estimation further enhances the proposed ML estimation by exploiting prior distributions of device activities and interference powers. Numerical results show the substantial gains of the proposed joint estimation designs, and reveal the importance of explicit consideration of interference and the value of prior information in device activity detection.

Session Chair

Minseok Choi (Jeju National University, United States)

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Session T3-S1

Learning 1

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

Robust Federated Learning Under Worst-case Model

Fan Ang (University of Science and Technology of China, China); Chen Li (University of Science And Technology of China, China); Weidong Wang (University of Science and Technology of China, China)

0

Federated learning provides a communication-efficient training process via alternating between local training and averaging updated local model. Nevertheless, it requires perfectly acquisition of the model which is hard to achieve in wireless communication practically, and the noise will cause serious effect on federated learning. To tackle this challenge, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in communication steps, we first formulate the problem as the parallel optimization for each node under worst-case model. We utilize the samplingbased successive convex approximation algorithm to develop a feasible training scheme, due to the unavailable maxima noise condition and non-convex issue of the objective function. In addition, the convergence rate of proposed design are analyzed from a theoretical point of view. Finally, the prediction accuracy improvement and loss function value reduction of the proposed design are demonstrated via simulation.

SADroid: A Deep Classification Model for Android Malware Detection Based on Semantic Analysis

Dali Zhu (Institute of Information Engineering, Chinese Academy of Sciences, China); Tong Xi (Institute of Information Engineering, Chinese Academy of Sciences & School of Cyber Security, University of Chinese Academy of Sciences, China); Pengfei Jing (Institute of Information Engineering, Chinese Academy of Sciences, China); Qing Xia (Institute of Software, Chinese Academy of Sciences, China); Di Wu and Yiming Zhang (Institute of Information Engineering, Chinese Academy of Sciences, China)

0

Previous works have designed many deep learning models for Android malware detection using various features (e.g. permissions, APIs et.) to achieve better classification performance. However, these methods usually input each feature into the classifier independently and completely (using One- Hot Encoding) so that features are orthogonal to each other. This discrete representation is difficult to preserve the semantic information of features. In this paper, we design two feature segmentation methods to enhance the semantics of the features in preprocessing. Besides that, we propose a malware detection model that consists of a distributed representation process for Android features and an optimized convolutional neural network for classification, named Semantic Analysis Detection (SADroid). In SADroid, the distance between features with similar semantics is closer in vector space. It provides the semantic information of features to the classifier to improve the classification performance. In the evaluation, SADroid outperforms the advanced models in detection accuracy on a data set of 19,600 applications, while maintaining a low computational cost.

Semi-Federated Learning

Zhikun Chen, Daofeng Li, Ming Zhao, Sihai Zhang and Jinkang Zhu (University of Science and Technology of China, China)

1

Federated learning(FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less uplink transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.

Optimization-driven Hierarchical Deep Reinforcement Learning for Hybrid Relaying Communications

Yuze Zou (Huazhong University of Science and Technology, China); Yutong Xie (Shenzhen Institutes of Advanced Technology, China); Canhui Zhang and Shimin Gong (Sun Yat-sen University, China); Hoang Thai Dinh (University of Technology Sydney (UTS), Australia); Dusit Niyato (Nanyang Technological University, Singapore)

2

In this paper, we employ multiple wireless-powered user devices as wireless relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. To improve energy efficiency, we design a hybrid relaying communication strategy in which wireless relays are allowed to operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall SNR by jointly optimizing the access point's beamforming strategy as well as individual relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure of the SNR maximization problem, we develop a deep reinforcement learning approach that adapts the beamforming and relaying strategies dynamically. In particular, we propose a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach that integrates the model-based optimization into the framework of conventional DDPG approach. It decomposes the discrete relay mode selection into the outer-loop by using deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relays' operating parameters by using the inner-loop DDPG algorithm. Simulation results reveal that the H-DDPG is robust to the hyper parameters and can speed up the learning process compared to the conventional DDPG approach.

Auction based Incentive Design for Efficient Federated Learning in Cellular Wireless Networks

Huong Tra Le (Kyung Hee University, Korea (South)); Nguyen H. Tran (The University of Sydney, Australia); Yan Kyaw Tun (Kyung Hee University, Korea (South)); Zhu Han (University of Houston, USA); Choong Seon Hong (Kyung Hee University, Korea (South))

0

Federated learning is an prominent machine learning technique that model is trained distributively by using local data of mobile users, which can preserve the privacy of users and still guarantee high learning performance. In this paper, we deal with the problem of incentive mechanism design for motivating users to participate in training. In this paper, we employ the randomized auction framework for incentive mechanism design in which the base station is a seller and mobile users are buyers. Concerning the energy cost incurred due to join the training, the users need to decide how many uplink subchannels, transmission power and CPU cycle frequency and then claim them in submitted bids to the base station. After receiving the submitted bids, the base station needs algorithms to select winners and determine the corresponding rewards so that the social cost is minimized. The proposed mechanism can guarantee three economic properties, i.e., truthfulness, individual rationality and efficiency. Finally, numerical results are provided to demonstrate the effectiveness, and efficiency of our scheme.

Session Chair

Jeongyeup Paek (Chung-Ang University, Korea (South))

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Session T3-S2

Sensor Network and IoT 1

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

Impact of Packet Routing Scheme on Post-Failure Industrial Wireless Sensor Networks

Rajith M Bandarage, Geoffrey G. Messier and Abraham O Fapojuwo (University of Calgary, Canada)

2

In most industrial applications, failed nodes will be repaired, but these repairs take time. For critical system applications, network operators need to understand how their networks function immediately after a failure but before a repair is possible. In this paper, we introduce an extension to the frame-level optimized routing/ scheduling algorithm to improve reliability and energy efficiency and compare it with the other industrial routing algorithms considering the oil refinery wireless sensor networks. We present a structured performance evaluation approach for studying the impact of the routing/scheduling algorithm on the post-failure/pre-repair regime. Simulation results show that the proposed algorithm exerts a positive impact on network performance: highly-reliable, low-latency, energy-efficient, and fitting with most industrial applications.

Wireless Backhaul Strategies for Real-Time High-Density Seismic Acquisition

Varun Amar Reddy and Gordon Stüber (Georgia Institute of Technology, USA); Suhail Al-Dharrab, Ali H Muqaibel and Wessam Mesbah (King Fahd University of Petroleum and Minerals, Saudi Arabia)

0

Modern geophysics methods for oil and gas exploration are able to generate subsurface images of superior quality and depth, albeit making real-time data acquisition a more challenging task. While current literature addresses data transfer issues primarily between the geophones and the gateway nodes, a communication scheme for the transfer of data from the gateway nodes to the sink largely remains unsolved. A novel wireless geophone network architecture for seismic data acquisition is described, with the objective of eliminating cable-based systems and providing Gigabit rates in order to support real-time acquisition. A performance analysis is conducted for various mesh networks employing IEEE 802.11ac, IEEE 802.11ad, and free space optical communication, from the geophysics perspective. Our findings suggest that the bottleneck links can be shifted from the top to the bottom of existing architectures, and a scalable approach requiring minimal number of gateway devices can be designed for high-density seismic surveys.

Asymmetric Wake-up Scheduling based on Block Designs in Wireless Sensor Networks

Teuk-Seob Song (Mokwon University, Korea (South)); Woosik Lee (SSIS, Korea (South)); Jong-Hoon Youn (University of Nebraska - Omaha, USA)

1

In wireless sensor networks (WSNs) with symmetric duty cycles, a block design technique produces an optimal solution for neighbor discovery in terms of the worst-case discovery latency. However, block design-based neighbor discovery methods may not be applicable to WSNs with asymmetric duty operations. Thus, to address this lack of support of asymmetric WSNs, we propose a new neighbor discovery protocol (NDP) that combines two block designs for generating a set of discovery schedules. We prove that the discovery schedule generated by the proposed NDP includes at least one common active slot with any neighboring nodes within a single cycle. We also conduct a simulation study and show that the proposed NDP is better than representative NDPs such as U-Connect, Disco, SearchLight, Hedis, and Todis in terms of discovery latency and energy efficiency.

On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

Kaushlendra Pandey (Indian Institute of Technology, Kanpur, India); Abhishek K Gupta (Indian Institute of Technology Kanpur, India)

0

In this paper, we consider wireless sensor networks (WSNs) with sensor nodes exhibiting clustering in their deployment. We model the coverage region of such WSNs by Boolean Poisson cluster models (BPCM) where sensors nodes' location is according to a Poisson cluster process (PCP) and each sensor has an independent sensing range around it. We consider two variants of PCP, in particular Matern and Thomas cluster process to form Boolean Matern and Thomas cluster models. We first derive the capacity functional of these models. Using the derived expressions, we compute the sensing probability of an event and compare it with sensing probability of a WSN modeled by a Boolean Poisson model where sensors are deployed according to a Poisson point process. We also derive the power required for each cluster to collect data from all of its sensors for the three considered WSNs. We show that a BPCM WSN has less power requirement in comparison to the Boolean Poisson WSN, but it suffers from lower coverage, leading to a trade-off between per-cluster power requirement and the sensing performance. A cluster process with desired clustering may provide better coverage while maintaining low power requirements.

FMUCR: Fuzzy-based Multi-hop Unequal Cluster Routing for WSN

Lu Sixu, Wu Muqing and Min Zhao (Beijing University of Posts and Telecommunications, China)

1

Recently, wireless sensor networks play an important role in our life. Cluster routing has gained more attention in wireless sensor networks. However, hotpot problem always exists. One way to solve this problem is the unequal cluster routing. In most of the unequal routing protocols, nodes which closer to base station have the smaller cluster size than others. It will reduce the relay pressure of the node which near to the base station. In this paper, we propose the fuzzy-based multi-hop unequal cluster routing. In the cluster head election phase, relative inter cluster cost and relative intra cluster cost are proposed innovatively. Fuzzy system is used for unequal clustering which reduces the energy consumption for cluster members. In the cluster formation phase, a novel probability mechanism is proposed to let the cluster members decide which cluster to join. In the multi-hop routing phase, relative relay cluster cost is proposed innovatively for inter cluster routing. Two factors are considered for multi-hop routing which reduces the energy consumption for cluster heads. Self-adaptive rotation mechanism is proposed in the data transmission phase. It reduces the frequency for re-clustering self-adaptively which reduces the control overhead of the entire network. According to the simulation results, the proposed protocol balances and reduces the energy consumption as well as extends the network lifetime of the whole network.

Session Chair

Wei Liu (Chongqing University of Technology, P.R. China)

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Session T3-S3

Fog Computing and Networking

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

A Collaborative Task Offloading Scheme in D2D-Assisted Fog Computing Networks

Nanxin Fan and Xiaoxiang Wang (Beijing University of Posts and Telecommunications, China); Dongyu Wang (Beijing University of Posts and Telecommunications & Key Laboratory of Universal Wireless Communications, Ministry of Education, China); Yanwen Lan and Junxu Hou (Beijing University of Posts and Telecommunications, China)

2

Fog computing is a promising way to deal with computation-extensive tasks by deploying distributed nodes at the network edge. However, the resources of fog nodes may not be enough when many users need to be served simultaneously. Therefore, task offloading between a device- to-device (D2D) pair is introduced in existing works, which ignores the computing resources of other ubiquitous users. In this paper, we study the partial task offloading in D2D-assisted fog computing networks, where the task is collaboratively executed by multiple D2D users or a Fog-Access Point (F-AP). Firstly, the social relationship based on task offloading is introduced for user incentives. Secondly, aiming to maximize the total revenue, an optimization problem is formulated with the joint consideration of offloading strategy, computation resource allocation and D2D user selection. Next, to solve this non-linear and non-convex problem, a potential game is formulated and the existence of a Nash equilibrium (NE) is proved. Then, we propose a joint optimization algorithm to obtain the NE, where computation resource allocation problem is solved by Lagrange Multiplier Method while D2D users are selected through greedy strategy. Finally, simulations show the effectiveness of the proposed scheme.

An Energy-Efficient Mixed-Task Paradigm in Resource Allocation for Fog Computing

Xincheng Chen (Xi'an Jiaotong University, China); Yuchen Zhou (Xidian University, China); Long Yang (Xidian University, China & University of Alberta, Canada); Lu Lv (Xidian University, China)

3

We study the energy efficiency problem for a fog computing system with multiple users and fog nodes. In order to handle various kinds of tasks at the same time, a mixed- task paradigm is proposed to combine both binary offloading and partial offloading. Then, a mixed-task resource allocation problem with the consideration of computation and communication resources is formulated as a mixed integer nonlinear programming problem (MINLP), which cannot be handled by the traditional relaxation algorithm due to the joint design of binary and partial offloading. To solve this problem efficiently, we first adopt a replacement-based method to transform the problem, and then design an augmented Lagrange method (ALM)-based resource allocation scheme. To further accelerate the solution procedure, a novel optimization technique, AMSGrad, is applied to the designed scheme. The performance of the proposed scheme is demonstrated by simulation results.

Latency Minimization with Optimum Workload Distribution and Power Control for Fog Computing

Saman Atapattu (University of Melbourne, Australia); Chathuranga Weeraddana (University of Moratuwa, Sri Lanka); Minhua Ding (Sri Lanka Institute of Information Technology, Sri Lanka); Hazer Inaltekin (Macquarie University, Australia); Jamie S Evans (University of Melbourne, Australia)

3

This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.

Mobility Prediction-Based Joint Task Assignment and Resource Allocation in Vehicular Fog Computing

Xianjing Wu, Shengjie Zhao and Rongqing Zhang (Tongji University, China); Liuqing Yang (Colorado State University, USA)

1

Most recently, vehicular fog computing (VFC) has been regarded as a novel and promising architecture to effectively reduce the computation time of various vehicular application tasks in Internet of vehicles (IoV). However, the high mobility of vehicles makes the topology of vehicular networks change fast, and thus it is a big challenge to coordinate vehicles for VFC in such a highly mobile scenario. In this paper, we investigate the joint task assignment and resource allocation optimization problem by taking the mobility effect into consideration in vehicular fog computing. Specifically, we formulate the joint optimization problem from a Min-Max perspective in order to reduce the overall task latency. Then we decompose the non- convex problem into two sub-problems, i.e., one to one matching and bandwidth resource allocation, respectively. In addition, considering the relatively stable moving patterns of a vehicle in a short period, we further introduce the mobility prediction to design a mobility prediction-based scheme to obtain a better solution. Simulation results verify the efficiency of our proposed mobility prediction-based scheme in reducing the overall task completion latency in VFC.

Distributed V2V Computation Offloading Based on Dynamic Pricing Using Deep Reinforcement Learning

Jinming Shi (Tsinghua University, China); Jun Du (Tsinghua University, Beijing, China); Jian Wang and Jian Yuan (Tsinghua University, China)

1

Vehicular computation offloading is a promising paradigm that improves the computing capability of vehicles to support autonomous driving and various on-board infotainment services. Comparing with accessing the remote cloud, distributed vehicle-to-vehicle (V2V) computation offloading is more efficient and suitable for delay-sensitive tasks by taking advantage of vehicular idle computing resources. Due to the high dynamic vehicular environment and the variation of available vehicular computing resources, it is a great challenge to design an effective task offloading mechanism to efficiently utilize vehicular computing resources. In this paper, we investigate the computation task allocation among vehicles, and propose a distributed V2V computation offloading framework, in which wireless channel states and variation of idle computing resources are both considered. Specially, we formulate the task allocation problem as a sequential decision making problem, which can be solved by using deep reinforcement learning. Considering that vehicles with idle computing resources may not share their computing resources voluntarily, we thus propose a dynamic pricing scheme that motivates vehicles to contribute their computing resources according to the price they receive. The performance of designed task allocation mechanism is validated by simulation results which reveal the effectiveness of our mechanism compared to the other algorithms.

Session Chair

Byung-Seo Kim (Hongik University, Korea)

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Session T4-S1

Streaming and Video

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

Distributed Video Analysis for Mobile Live Broadcasting Services

Yuanqi Chen (Nanjing University Of Science And Technology, China); Yongjie Guan (UNC Charlotte, USA); Tao Han (University of North Carolina at Charlotte, USA)

0

While webcast platforms on mobile devices are becoming more and more prevalent, inspection for irregularities is getting harder and harder. To solve this problem, the convolution neural network(CNN) has been applied to recognize or detect specified objections in pictures and videos. However, when supervising large platforms, it isn't very easy to collect mountain piles of video data and send them to the computation center. Other problems like long time delay and the high computational burden will reduce system performance, especially when dealing with data from live streams. This paper presents a method to coordinate mobile devices with remote servers(computers or embedded systems) to achieve real-time monitoring of live streams. The system can make use of computational capacity on mobile devices and reduce the cost of sending data while guaranteeing accuracy for supervision.

Optimal Buffering for High Quality Video Streaming in D2D Underlay Networks

Suvadip Batabyal (BITS Pilani, Hyderabad Campus, India); Ozgur Ercetin (Sabanci University, Turkey)

0

Device-to-device (D2D) communication helps in enhancing the capacity of the cellular network. However, the provision of video streaming in a D2D underlay network is challenging due to the dynamic and limited availability of resources especially under high mobility. Scalable video coding (SVC) allows for dynamic adjustment of video quality level according to the instantaneous network conditions, e.g., achievable data rate, player buffer occupancy and user preferences. In this paper, we propose an optimal decision theory (ODT) based scheme to fill the buffers with appropriate video quality levels so as to minimize the absolute distortion under constrained stall percent. A setup with one-pair of D2D user and other cellular users (CUs) sharing the same bandwidth with different mobility patterns is used to evaluate the proposed scheme. The scheme is compared with two other schemes viz., the random allocation scheme and the greedy allocation scheme to observe the performance of the ODT based scheme.

Joint Quality Selection and Caching for SVC Video Services in Heterogeneous Networks

Jianwen Meng, Hancheng Lu and Jinxue Liu (University of Science and Technology of China, China)

0

Edge caching has been regarded as an effective way to relieve backhaul pressure as well as reduce service delivery delay. In this paper, we attempt to improve user's quality of experience (QoE) of video services in cache-enabled heterogeneous networks (HetNets). Scalable video coding (SVC) based video services are considered and hence the proper video quality can be selected for each user according to its channel conditions. Intuitively, caching video layers that cannot be delivered to users leads to the waste of limited cache resource. Based on this observation, we formulate the joint video quality selection and caching problem, with the goal to maximize user's QoE. Furthermore, we propose a two-fold dynamic programming algorithm to approach the optimal video quality selection and caching strategies. Finally, the simulation results demonstrate that the proposed strategies can utilize the limited cache space more effectively and achieve more QoE gains compared to existing caching strategies.

Reducing Latency in Interactive Live Video Chat Using Dynamic Reduction Factor

YangXin Zhao, Anfu Zhou (Beijing University of Posts and Telecommunications, China); Xiaojiang Chen (Alibaba Group, China)

0

Live video traffic is taking an increasing share of Internet traffic in recent years. To provide the best user experience, jitter buffers are employed to eliminate the effects of network jitter and to enable smooth video playback. At the same time, an additional delay is added, namely jitterdelay. In this work, we examine how jitter buffer performs in the Web Real-Time Communications (WebRTC), which is the de-facto standard used in mainstream browsers for interactive video chat applications. We collect a dataset from a video live streaming service provider, which adopts WebRTC. After an in-depth analysis of the dataset, we find that jitter buffer can dynamically adjust the jitterdelay but is too conservative, resulting in a very slow decline of jitterdelay. To address the issue, we analyze the control logic of the jitter buffer and find that it uses a fixed reduction factor, known as psi (\psi), which causes the problem. To handle the problem, we propose an enhanced jitter buffer adaptation mechanism called JTB-\psi, which dynamically adjusts \psi according to the size and the duration of the large frame, to reasonably speed up the decline of jitterdelay. Practical testbed experiments show that JTB-\psi achieves a 41.5\% lower jitterdelay and improves receiving frame rate, quantization parameter (QP) and sending bit-rate under different network conditions, compared to the fixed-\psi approach.

QFR: A QoE-driven Fine-grained Routing Scheme for Virtual Reality Video Streaming over SDN

Xiaoyu Liu, Yumei Wang and Yu Liu (Beijing University of Posts and Telecommunications, China)

0

In order to meet the Quality of Experience (QoE) requirements of Virtual Reality (VR) video users under limited resources, efficient and adaptive routing scheme is required. The next generation mobile networks 5G can match network and computing resources according to service requirements, which will be the communication technology for the VR industry. In 5G architecture, the introduction of Software Defined Networking (SDN) decouples the control plane and the forwarding plane, and provides the ability of more granular network resource management. It can actively allocate resources for VR video to optimize transmission performance. In this paper, a QoE-driven Fine-grained routing (QFR) scheme based on SDN has been proposed. The core of QFR is the route calculation algorithm and the route allocation strategy. The route calculation algorithm is a two-stage adaptive routing algorithm. In the first stage, by means of an improved Dijkstra algorithm, the algorithm calculates k paths with the shortest delay. In the second stage, the k paths with the shortest delay are ranked according to the predicted QoE of each path. In addition, tile-based VR video provides a prerequisite for fine-grained routing scheduling. Through differentiated routing of Field of View (FoV) video streaming and Non-FoV video streaming, we develope a fine-grained route allocation strategy. The route allocation strategy determines how to allocate the sorted k paths with the shortest delay according to the residual bandwidth. Comparative evaluation of QFR is conducted to show its preponderance over several existing routing schemes, in terms of download bitrate and QoE of VR video.

Session Chair

Youngbin Im (UNIST, Korea)

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Session T4-S2

Security and Privacy

Conference

11:00 AM — 12:30 PM KST

Local

May 25 Mon, 10:00 PM — 11:30 PM EDT

Blockchain and Stackelberg Game Model for Roaming Fraud Prevention and Profit Maximization

Cong Thanh Nguyen (Ho Chi Minh City University of Technology, VNU-HCM, Vietnam); Diep N. Nguyen (University of Technology Sydney, Australia); Hoang Thai Dinh (University of Technology Sydney (UTS), Australia); Hoang-Anh Pham (Ho Chi Minh City University of Technology & Vietnam National University Ho Chi Minh City, Vietnam); Nguyen Huynh Tuong (Faculty of Computer Science & Engineering, Ho Chi Minh city University of Technology, Vietnam); Eryk Dutkiewicz (University of Technology Sydney, Australia)

0

Roaming fraud is one of the most significant financial losses for mobile service providers. The inefficiency of current exchanging data management methods among mobile service providers is the main obstacle for roaming fraud prevention. In this paper, we introduce a novel blockchain-based data exchange management system to address roaming fraud problems in mobile networks. This system provides a secure and automatic data exchange service among mobile service providers and mobile subscribers. In addition, we introduce an emerging Proof-of-Stake (PoS) consensus mechanism for the proposed blockchain-based roaming fraud prevention system, which can significantly reduce the delay in exchanging information as well as implementation costs for mobile service providers. To further enhance benefits and security efficiency for the proposed blockchain system, we develop an economic model based on Stackelberg game. This game model is very effective in maximizing profits for both the stakeholders and stake pool and useful in designing a robust blockchain-based mobile roaming management system. Through performance analysis and numerical results, we show that our proposed framework not only provides an effective solution to prevent mobile roaming fraud but also opens many business opportunities for future mobile networks.

A Secure Session Key Negotiation Scheme in WPA2-PSK Networks

Jiang Guo, Miao Wang, Hanwen Zhang and Yujun Zhang (Institute of Computing Technology, Chinese Academy of Sciences, China)

0

This talk does not have an abstract.

Weighted Trustworthiness for ML Based Attacks Classification

Zina Chkirbene (Qatar University & Electrical Engineering, Qatar); Aiman Erbad, Ridha Hamila, Ala Gouissem, Amr Mohamed and Mohsen Guizani (Qatar University, Qatar); Mounir Hamdi (Hamad Bin Khalifa University, Qatar)

0

Recently, machine learning techniques are gaining a lot of interest in security applications as they exhibit fast processing with real-time predictions. One of the significant challenges in the implementation of these techniques is the collection of a large amount of training data for each new potential attack category, which is most of the time, unfeasible. However, learning from datasets that contain a small training data of the minority class usually produces a biased classifiers that have a higher predictive accuracy for majority class(es), but poorer predictive accuracy over the minority class. In this paper, we propose a new designed attacks weighting model to alleviate the problem of imbalanced data and enhance the accuracy of minority classes detection. In the proposed system, we combine a supervised machine learning algorithm with the node1 past information. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. Then, the system stores these decisions in a database and exploits them for the weighted attacks classification model. Thus, for each attack class, the weight that maximizes the detection of the minority classes will be computed and the final combined decision is generated. In this work, we use the UNSW dataset to train the supervised machine learning model. The simulation results show that the proposed model can effectively detect intrusion attacks and provide better accuracy, detection rates and lower false alarm rates compared to state-of-the art techniques.

Wearable Proxy Device-Assisted Authentication Request Filtering for Implantable Medical Devices

Ziting Zhang (Beijing University of Posts and Telecommunications, China); Xiaodong Xu (Beijing University of Posts and Telecommunications & Wireless Technology Innovation Institute, China); Shujun Han and Yacong Liang (Beijing University of Posts and Telecommunications, China); Cong Liu (China Mobile Research Institute, China)

0

As the deepening of 5G's support for the e-health industry, more and more wireless medical devices will suffer from various attacks and threats. Especially, the security of implantable medical devices (IMDs) which have limited computational capabilities and stringent power constraints becomes a critical issue. According to the channel state information, we exploit the special characteristics of the received signal strength (RSS) ratio between wearable proxy devices (WPDs) and IMDs in wireless body area networks (WBANs) to distinguish legitimate users and attackers. Moreover, based on the idea of proposed authentication request filtering (ARF), we design two corresponding light-weight security protocols to defend the forced authentication (FA) attacks and enhance the accessibility of IMD in emergency mode respectively. Simulation results show that the proposed ARF scheme to defend FA attacks achieves a high authentication response rate (ARR) with 99.2% for legitimate users and a low ARR with 2.4% for attackers at the maximum gap threshold point. Furthermore, when applied in emergency mode, the ARF scheme allows up to 96.3% emergency rescue devices to access the IMDs with only one attempt.

A New Privacy-Preserving Framework based on Edge-Fog-Cloud Continuum for Load Forecasting

Shiming Hou and Hongjia Li (Chinese Academy of Sciences, China); Chang Yang (Institute of Information Engineering, Chinese Academy of Sciences, China); Liming Wang (Chinese Academy of Sciences, China)

0

As an essential part to intelligently fine-grained scheduling, planning and maintenance in smart grid and energy internet, short-term load forecasting makes great progress recently owing to the big data collected from smart meters and the leap forward in machine learning technologies. However, the centralized computing topology of classical electric information system, where individual electricity consumption data are frequently transmitted to the cloud center for load forecasting, tends to violate electric consumers' privacy as well as to increase the pressure on network bandwidth. To tackle the tricky issues, we propose a privacy-preserving framework based on the edge-fog- cloud continuum for smart grid. Specifically, 1) we gravitate the training of load forecasting models and forecasting workloads to distributed smart meters so that consumers' raw data are handled locally, and only the forecasting outputs that have been protected are reported to the cloud center via fog nodes; 2) we protect the local forecasting models that imply electricity features from model extraction attacks by model randomization; 3) we exploit a shuffle scheme among smart meters to protect the data ownership privacy, and utilize a re-encryption scheme to guarantee the forecasting data privacy. Finally, through comprehensive simulation and analysis, we validate our proposed privacy-preserving framework in terms of privacy protection, and computation and communication efficiency.

Session Chair

Hyang-Won Lee (Konkuk University, Korea)

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Session T1-S4

NOMA (Non-Orthogonal Multiple Access)

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Covert Non-Orthogonal Multiple Access

Hien Ta and Sang Wu Kim (Iowa State University, USA)

4

We consider hiding a covert (private) message in non-orthogonal multiple access (NOMA) systems by superimposing (embedding) it under non-covert (public) messages. We determine the total detection error probability (sum of false alarm and missed detection probability), the adversary's optimum detection strategy that minimizes the total detection error probability, and the communicator's optimum message hiding strategy that maximizes the total detection error probability. Additionally, we explore exploiting the channel variations to further increase the total detection error probability. We show that the total detection error probability increases and converges to 1 as the number of users increases and that the total detection error probability can be increased by increasing the transmission power when the channel variation is exploited.

Finite-Alphabet Signature Design for Grant-Free NOMA using Quantized Deep Learning

Hanxiao Yu and Fei Zesong (Beijing Institute of Technology, China); Zhong Zheng (National Institute of Standards and Technology, USA); Neng Ye (Beijing Institute of Technology, China); Sen Wang (China Mobile Research Institute, China)

1

Grant-free Non-Orthogonal Multiple Access (NOMA) techniques are able to reduce the signaling overhead and the transmission latency in multi-user communications system. However, most of the existing code-domain grant-free NOMA schemes reuse the spreading signatures designed for the grant- based scenarios. Considering the sparsity and randomness nature of user activities in the uplink transmissions, we propose a deep learning-based signature design, where the non-equal user activation probabilities are exploited to optimize the code-domain NOMA signature. In addition, the conventional grant-free NOMA signatures are not specifically designed over finite Galois field, which hinders the implementation of the encoder/decoder using practical hardware. To address these challenges, we utilize the quantized deep learning framework for the NOMA signature training, which jointly optimizes the sequence generation and the quantization. The numerical results reveal that the obtained signatures outperform the conventional ones especially when the users has unequal activation probabilities.

Physical Layer Secrecy of NOMA-Based Hybrid Satellite-Terrestrial Relay Networks

Vinay Bankey, Vibhum Singh and Prabhat Kumar Upadhyay (Indian Institute of Technology Indore, India)

2

In this paper, we investigate the physical layer security of a non-orthogonal multiple access (NOMA) based downlink hybrid satellite-terrestrial relay network where the satellite communicates with terrestrial NOMA users in the presence of an eavesdropper via an amplify-and-forward relay. To analyze the secrecy performance of this network, we derive new closed- form expressions of secrecy outage probability (SOP) and the probability of positive secrecy capacity by adopting shadowed-Rician fading for the satellite link and Nakagami-m fading for terrestrial links. To get more insights, we further derive the closed- form expression for asymptotic SOP and illustrate the behavior of SOP at high signal-to-noise ratio regime. The analytical results are corroborated through Monte-Carlo simulations.

Performance Analysis of the Virtual Full-Duplex Non-Orthogonal Multiple Access Systems

Yafang Zhang and Suili Feng (South China University of Technology, China)

4

Full-duplex (FD) and non-orthogonal multiple access (NOMA) are the principal means to improve the spectral efficiency of the system. In this paper, a virtual full-duplex NOMA (VFD-NOMA) scheme is proposed, in which a pair of geographically distributed half-duplex remote radio units (RRU) are used to imitate the traditional FD-enabled BS and to serve the uplink and downlink users simultaneously with the centralized control of the baseband processing unit (BPU). We investigate the outage probability and ergodic sum rate of the proposed scheme and obtain their closed-form expressions with perfect and imperfect interference cancellation. Simulation results not only show the feasibility of the developed analysis, but also unveil the performance gains achieved by the proposed scheme over the conventional full-duplex NOMA scheme.

Compressive Sensing based User Activity Detection and Channel Estimation in Uplink NOMA Systems

Yuanchen Wang (University of Liverpool, United Kingdom (Great Britain)); Xu Zhu (University of Liverpool, United Kingdom (Great Britain) & Harbin Institute of Technology, Shenzhen, China); Eng Gee Lim (Xi'an Jiaotong-Liverpool University, China); Zhongxiang Wei (University College London & School of EE and CS, United Kingdom (Great Britain)); Yujie Liu (University of Liverpool, United Kingdom (Great Britain)); Yufei Jiang (Harbin Institute of Technology, Shenzhen, China)

3

Conventional request-grant based non-orthogonal multiple access (NOMA) incurs tremendous overhead and high latency. To enable grant-free access in NOMA systems, user activity detection (UAD) is essential. In this paper, we investigate compressive sensing (CS) aided UAD, by utilizing the property of quasi-time-invariant channel tap delays as the prior information. This does not require any prior knowledge of the number of active users like the previous approaches, and therefore is more practical. Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. They achieve much higher UAD accuracy than the previous work at low signal-to-noise ratio (SNR). Based on the UAD results, we also propose a low-complexity CS based channel estimation scheme, which achieves higher accuracy than the previous channel estimation approaches.

Session Chair

Wonjae Shin (Pusan National University, Korea (South))

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Session T1-S5

Next-Generation Communications

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Chemical Reactions-based Detection Mechanism for Molecular Communications

Trang N Cao (University of Melbourne, Australia); Vahid Jamali (Friedrich-Alexander-University Erlangen-N¨urnberg, Germany); Wayan Wicke (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany); Phee Lep Yeoh (University of Sydney, Australia); Nikola Zlatanov (Monash University, Australia); Jamie S Evans (University of Melbourne, Australia); Robert Schober (Friedrich-Alexander University Erlangen-Nuremberg, Germany)

2

In molecular communications, the direct detection of signaling molecules may be challenging due to the lack of suitable sensors and interference from co-existing substances in the environment. Motivated by examples in nature, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. The underlying reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed-form. To analyze these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables in each time step. We also derive insightful closed-form solutions for a special case. The accuracy of the proposed algorithm is verified by particle-based simulations. Our results show that the concentration of the product molecules has a similar characteristic over time as the concentration of the signaling molecules. We analyze the bit error rate (BER) for a threshold detector and highlight that significant improvements in the BER can be achieved by carefully choosing the molecular probe and optimizing the detection threshold.

Generalized Dimming Control Scheme with Optimal Dimming Control Pattern for VLC

Congcong Wang, Yang Yang, Caili Guo, Zhimin Zeng and Chunyan Feng (Beijing University of Posts and Telecommunications, China)

1

This paper proposes a simple dimming control scheme for multi-LED visible light communications (VLC), termed as generalized dimming control (GDC) scheme. The GDC performs dimming control by simultaneously adjusting the intensity of transmitted symbols and the number of active elements in a space-time matrix. The indices of active elements in each space-time matrix, as well as the modulated constellation symbols, are used to transmit information base on the space-time index modulation. Furthermore, the tradeoff between the intensity of transmitted symbols and the number of active elements in the space-time matrix is analyzed for enhancing the reliability of communication. To improve the communication reliability, a GDC with the minimum bit error rate (BER) criterion is first proposed to select the optimal dimming control pattern based on exhaustive search. Furthermore, to reduce the computational complexity, a low complexity GDC with the maximum free distance criterion is proposed. Simulation and numerical results show that the proposed scheme can outperform conventional dimming control schemes at both low and high dimming levels in terms of the BER performance.

Characterization for High-Speed Railway Channel enabling Smart Rail Mobility at 22.6 GHz

Lei Ma, Ke Guan, Dong Yan, Danping He and Bo Ai (Beijing Jiaotong University, China); Junhyeong Kim and Hee Sang Chung (ETRI, Korea (South))

1

The millimeter wave (mmWave) communication with large bandwidth is a key enabler for both the fifth- generation mobile communication system (5G) and smart rail mobility. Thus, in order to provide realistic channel fundamental, the wireless channel at 22.6 GHz is characterized for a typical high-speed railway (HSR) environment in this paper. After importing the three-dimensional environment model of a typical HSR scenario into a self-developed high-performance cloud- computing Ray-Tracing platform - CloudRT, extensive ray-tracing simulations are realized. Based on the results, the HSR channel characteristics are extracted and analyzed, considering the extra loss of various weather conditions. The results of this paper can help for the design and evaluation for the HSR communication systems enabling smart rail mobility.

Transmit laser selection for dual hop decode and forward UOWC cooperative communication

Anirban Bhowal and Rakhesh Singh Kshetrimayum (Indian Institute of Technology Guwahati, India)

1

Underwater optical wireless communication (UOWC) is an alternative to the existing acoustic and radio frequency (RF) underwater communication. In this paper, advanced schemes of UOWC like transmit laser selection (TLS) and TLS combined with optical spatial modulation (TLS-OSM) are proposed for weak oceanic turbulence based UOWC cooperative communication, which can enhance the performance of conventional UOWC communication. The performance is analyzed in terms of average symbol error probability (ASEP) and corroborated by Monte Carlo simulations. It is observed that TLS can achieve a signal to noise ratio (SNR) gain of at least 2 dB over the existing methods while TLS-OSM achieves a SNR gain of at least 3 dB over TLS scheme at any ASEP value of more than 10-2, thereby validating the efficiency of our schemes.

Hybrid multiplexing in OFDM-based VLC systems

Cheng Chen (University of Edinburgh, United Kingdom (Great Britain)); Iman Tavakkolnia (University of Edinburgh & LiFi Research and Development Centre, United Kingdom (Great Britain)); Mohammad Dehghani Soltani and Majid Safari (University of Edinburgh, United Kingdom (Great Britain)); Harald Haas (The University of Edinburgh, United Kingdom (Great Britain))

3

In conventional visible light communication (VLC) systems with multiple light-emitting diodes (LEDs) and multiple photodiodes (PDs), high data rate transmission with limited modulation bandwidth can be achieved via spatial multiplexing (SMP) or wavelength division multiplexing (WDM). However, the number of multiplexing channels is limited by the strong spatial correlation in SMP and by the inter-colour crosstalk in WDM. In this paper, we propose a multiple-input multiple-output (MIMO) hybrid multiplexing (HMP) VLC system which avoids the disadvantages of SMP / WDM and explores the degrees-of- freedom (DoFs) in space and wavelength domains jointly. With appropriate system configuration, a MIMO channel matrix with a better channel condition in HMP can be obtained. Eventually, it is able to increase the number of multiplexing channels and support higher data rate transmission.

Session Chair

Yo-Seb Jeon (POSTECH, Korea (South))

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Session T1-S6

Channel Modeling

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Joint Channel Equalization and Symbol Detection for IoT Devices in Severe Multipath Channels

Shusen Jing, Joseph Hall, Yahong Rosa Zheng and Chengshan Xiao (Lehigh University, USA); Zhiqun Deng (Pacific Northwest National Laboratory, USA)

0

Internet of Things (IoT) devices often transmit very short message blocks without strong error correction coding or pilots for channel equalization. When the transmitted signals encounter a severe multipath channel, the receiver is often unable to equalize the resulting inter-symbol interference (ISI) via traditional methods, leading to a high retransmission rate. This paper proposes a joint channel equalization and symbol detection scheme for this scenario by utilizing the preamble sequence as a training pilot for equalization and the relatively weak cyclic redundancy check (CRC) 8-Dallas code for error correction. By assuming a sparse channel impulse response, the proposed joint channel equalization and symbol detection scheme formulates an 11 norm constrained optimization problem and solves it by a saddle-point algorithm. The proposed algorithm is applied to a set of real-world underwater animal tracking IoT data, and the results show improvement of correct message detection rate from 21.5% (direct symbol decisions) to 41.6% (equalization and decoding).

A Novel Massive MIMO Beam Domain Channel Model

Fan Lai (Southeast University, China); Cheng-Xiang Wang (Southeast University & Heriot-Watt University, China); Jie Huang and Xiqi Gao (Southeast University, China); Fu-Chun Zheng (University of York, United Kingdom (Great Britain) & Southeast University, China)

1

A novel beam domain channel model (BDCM) for massive multiple-input multiple-output (MIMO) communication systems has been proposed in this paper. The near-field effect and spherical wavefront are firstly assumed in the proposed model, which is different from the conventional BDCM for MIMO based on the far-field effect and plane wavefront assumption. The proposed novel BDCM is the transformation of an existing geometry-based stochastic model (GBSM) from the antenna domain into beam domain. The space-time non-stationarity is also modeled in the novel BDCM. Moreover, the comparison of computational complexity for both models is studied. Based on the numerical analysis, comparison of cluster-level statistical properties between the proposed BDCM and existing GBSM has shown that there exists little difference in the space, time, and frequency correlation properties for two models. Also, based on the simulation, coherence bandwidths of the two models in different scenarios are almost the same. The computational complexity of the novel BDCM is much lower than the existing GBSM. It can be observed that the proposed novel BDCM has similar statistical properties to the existing GBSM at the cluster- level. The proposed BDCM has less complexity and is therefore more convenient for information theory and signal processing research than the conventional GBSMs.

Geometry based Stochastic Channel Modeling using Ambit Processes

Rakesh R. T. and Emanuele Viterbo (Monash University, Australia)

3

The simulation of vehicular wireless channels using geometry-based radio channel models is computationally intensive when the number of scatterers is significantly high. In this paper, we propose a new geometry-based stochastic channel model to simulate and analyze the aforementioned channels based on a framework developed from the theory of ambit processes. Under reasonable assumptions, the underlying mathematical structure of the proposed channel model enables the characterization of high mobility channels in terms of fading statistics, spatio-temporal channel correlation, and Doppler spectrum, besides ensuring tractable analysis. The developed algorithm facilitates fast simulation of high mobility channels and accounts for key features of vehicular channels including appearance and disappearance of multi-path components, spatial consistency, and captures the correlation between time-evolving delay and Doppler associated with multi-path components. Finally, we carry out simulations to obtain crucial insights about the characteristics of typical vehicle-to-infrastructure channels based on the proposed channel model.

A Practical Non-Stationary Channel Model for Vehicle-to-Vehicle MIMO Communications

Weidong Li and Qiuming Zhu (Nanjing University of Aeronautics and Astronautics, China); Cheng-Xiang Wang (Southeast University & Heriot-Watt University, China); Fei Bai, Xiao-min Chen and Xu Dazhuan (Nanjing University of Aeronautics and Astronautics, China)

0

In this paper, a practical model for non-stationary Vehicle-to-Vehicle (V2V) multiple-input multiple-output (MIMO) channels is proposed. The new model considers more accurate output phase of Doppler frequency and is simplified by the Taylor series expansions. It is also suitable for generating the V2V channel coefficient with arbitrary velocities and trajectories of the mobile transmitter (MT) and mobile receiver (MR). Meanwhile, the channel parameters of path delay and power are investigated and analyzed. The closed-form expressions of statistical properties, i.e., temporal autocorrelation function (TACF) and spatial cross-correlation function (SCCF) are also derived with the angle of arrival (AoA) and angle of departure (AoD) obeying the Von Mises (VM) distribution. In addition, the good agreements between the theoretical, simulated and measured results validate the correctness and usefulness of the proposed model.

Effects On Polarization Characteristics of Off-Body Channels with Dynamic Users

Kenan Turbic (INESC-ID / IST, University of Lisbon, Portugal); Luis M. Correia (IST/INESC-ID - University of Lisbon & INESC, Portugal)

0

This paper presents an analysis of the depolarization effect in off-body channels, based on a previously developed geometry-based channel model for polarized communications with dynamic users. The model considers Line-of-Sight propagation and components reflected from scatterers distributed on cylinders centered around the user. A mobility model for wearable antennas based on Fourier series is employed to take the effects of user's motion into account. The focus is on scattered signal components, where the impact of a scatter's position, its material properties, and the influence of user dynamics on signal depolarization are investigated. It is observed that the wearable antenna motion has a strong impact on the channel's polarization characteristics, particularly for dynamic on-body placements, such as arms and legs. If the antenna motion is neglected, the error in cross-polarization ratio is greater than 23 dB compared to a static approach. The antenna rotation during motion is found to be the dominant factor, while the corresponding displacement can be neglected, with the error not exceeding 1 dB. This result justifies the channel model simplification proposed in this paper.

Session Chair

Inkyu Bang (Hanbat National University, Korea (South))

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Session T1-S7

Waveform and Modulation

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Cancellation Symbol Insertion for Spectrally Compact OFDM Pilot Waveform

Char-Dir Chung (National Taiwan University, Taiwan); Wei-Chang Chen (National Taipei University of Technology, Taiwan); Jia-Ling Jiang (MediaTek Inc., Taiwan)

0

Cancellation symbols (CSs) are inserted into guard subcarriers to suppress the sidelobe power of the OFDM pilot waveform for channel estimation. Different from the conventional schemes where CSs are designed to suppress the sidelobe power of composite OFDM waveforms conveying data and pilot symbols, the inserted CSs depend on pilot symbols only and are shown to make the spectrally compact pilot waveform at the cost of less cancellation symbol power. The composite OFDM waveforms conveying spectrally precoded data symbols, optimum pilot symbols, and pilot-dependent CSs are shown to exhibit highly compact spectrum, while maintaining the channel estimation optimality on the quasi-static multipath channel.

Low Complexity Iterative Rake Detector for Orthogonal Time Frequency Space Modulation

Tharaj Thaj and Emanuele Viterbo (Monash University, Australia)

3

This paper presents a linear complexity iterative rake detector for the recently proposed orthogonal time frequency space (OTFS) modulation scheme. The basic idea is to extract and combine the received multipath components of the transmitted symbols in the delay-Doppler grid using linear diversity combining schemes like maximal ratio combining (MRC), equal gain combining and selection combining to improve the SNR of the combined signal. We reformulate the OTFS input-output relation in the vector form by placing some null symbols in the delay-Doppler grid thereby exploiting the block circulant property of the channel matrix. Using the new input-output relation we propose a low complexity iterative detector based on the MRC scheme. The bit error rate (BER) performance of the proposed detector will be compared with the state of the art message passing detector and orthogonal frequency division multiplexing (OFDM) scheme employing a single tap minimum mean square error (MMSE) equalizer. We also show that the frame error rate (FER) performance of the MRC detector can be improved by employing error correcting codes operating in the form of a turbo decision feedback equalizer (DFE).

The Impact of CFO on OFDM based Physical-layer Network Coding with QPSK Modulation

Ling Fu Xie (Faculty of EECS, Ningbo University, China); Ivan Wang-Hei Ho and Zhenhui Situ (The Hong Kong Polytechnic University, Hong Kong); Peiya Li (Jinan University, China)

0

This paper studies Physical-layer Network Coding (PNC) in a two-way relay channel (TWRC) operated based on OFDM and QPSK modulation but with the presence of carrier frequency offset (CFO). CFO, induced by node motion and/or oscillator mismatch, causes inter-carrier interference (ICI) that impairs received signals in PNC. Our ultimate goal is to empower the relay in TWRC to decode network-coded information of the end users at a low bit error rate (BER) under CFO, as it is impossible to eliminate the CFO of both end users. For that, we first put forth two signal detection and channel decoding schemes at the relay in PNC. For signal detection, both schemes exploit the signal structure introduced by ICI, but they aim for different output, thus differing in the subsequent channel decoding. We then consider CFO compensation that adjusts the CFO values of the end nodes simultaneously and find that an optimal choice is to yield opposite CFO values in PNC. Particularly, we reveal that pilot insertion could play an important role against the CFO effect, indicating that we may trade more pilots for not just a better channel estimation but also a lower BER at the relay in PNC. With our proposed measures, we conduct simulation using repeat-accumulate (RA) codes and QPSK modulation to show that PNC can achieve a BER at the relay comparable to that of point-to-point transmissions for low to medium CFO levels.

A Novel Hybrid MSK Modulation Scheme for Additional Data Transmission

Zhengguang Xu (Huazhong University Of Science and Technology, China)

0

In the paper, a novel hybrid modulation scheme is proposed to embed a phase-modulated signal into the minimum shift keying (MSK) signal, where the additional signal could be used to transmit some additional important data. In order to achieve the imperceptibility of the additional signal to the MSK signal, the orthogonal condition between the additional signal and the MSK signal is investigated. The bit-error-rate (BER) performances of the hybrid modulation over addition white Gaussian noise (AWGN) channel are also investigated. The theoretical analysis and simulation results show that the additional signal with the orthogonal frequency makes little effect on the BER performance of the original MSK receiver, and the information in the additional signal is also robust.

Performance Analysis of Various Waveforms and Coding Schemes in V2X Communication Scenarios

Waqar Anwar (Technische Universität Dresden, Germany); Anton Krause (TU Dresden, Germany); Atul Kumar, Norman Franchi and Gerhard P. Fettweis (Technische Universität Dresden, Germany)

0

5G and beyond communications systems need to cope with a high degree of heterogeneity in terms of services and requirements. Specially, vehicle-to-everything (V2X) use cases require ultra-reliable and low latency communications (URLLC) under harsh channel conditions. To design an optimal waveform and coding scheme for such use cases is a key challenge. Therefore, new waveforms and coding techniques are need to be investigated. In this paper, we present a comparison of several waveform candidates (orthogonal frequency-division multiplexing (OFDM), discrete Fourier transform-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM) and orthogonal time frequency space (OTFS)) and coding schemes (convolution, turbo, low-density priority-check (LDPC) and polar) under a common framework. We consider two metrics, i.e. maximum data rates and packet error rate, to evaluate their performance under various fading conditions. The simulation results show that OTFS outperforms all other waveforms in both frequency selective and doubly selective channels. Regarding the coding schemes, turbo codes outperforms all other coding schemes, even though difference with LDPC codes is marginal.

Session Chair

Joon Ho Cho (Pohang University of Science and Technology (POSTECH), Korea (South))

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Session T2-S2

Learning-Assisted Optimization

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

Chen Xu and Jian Wang (Huawei Technologies Co., Ltd., P.R. China); Tianhang Yu (Huawei Technologies Co., Ltd., China); Chuili Kong, Yourui Huangfu and Rong Li (Huawei Technologies Co., Ltd., P.R. China); Yiqun Ge (Huawei Technologies Canada Inc., Canada); Jun Wang (Huawei Technologies Co., Ltd., P.R. China)

2

In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic search methods are employed to explore the solution space. A deep reinforcement learning (DRL) framework with Advantage actor-critic (A2C) algorithm is proposed for the optimization problem. Several methods have been utilized in the framework to improve the sampling and training efficiency and to adapt the algorithm to a specific scheduling problem. Numerical results show that DRL outperforms the baseline algorithm and achieves similar performance as genie-aided methods without using the future information.

Jointly Sparse Signal Recovery via Deep Auto-encoder and Parallel Coordinate Descent Unrolling

Shuaichao Li and Wanqing Zhang (Shanghai Jiao Tong University, China); Ying Cui (Shanghai Jiaotong University, China)

0

In this paper, combining techniques in compressed sensing, parallel optimization and deep learning, an auto- encoder-based approach is proposed to jointly design the common measurement matrix and jointly sparse signal recovery method for complex sparse signals. The encoder achieves noisy linear compression for jointly sparse signals, with a common measurement matrix. The decoder realizes jointly sparse signal recovery based on an iterative parallel-coordinate descent algorithm which is proposed to solve GROUP LASSO in a parallel manner. In particular, the decoder consists of an approximation part which unfolds (several iterations of) the proposed iterative algorithm to obtain an approximate solution of GROUP LASSO and a correction part which reduces the difference between the approximate solution and the actual jointly sparse signals. To our knowledge, this is the first time that an optimization-based jointly sparse signal recovery method is implemented using a neural network. The proposed approach achieves higher recovery accuracy with less computation time than the classic GROUP LASSO method, and the gain significantly increases in the presence of extra structures in sparse patterns. The common measurement matrix obtained by the proposed approach is also suitable for the classic GROUP LASSO method. We consider an application example, i.e., channel estimation in Multiple-Input Multiple-Output (MIMO)-based grant-free massive access for massive machine-type communications (mMTC). By numerical results, we demonstrate the substantial gains of the proposed approach over GROUP LASSO and AMP when the number of jointly sparse signals is not very large.

Deep Reinforcement Learning based Indoor Air Quality Sensing by Cooperative Mobile Robots

Zhiwen Hu (Peking University, China); Tiankuo Song (Beijing 101 Middle School, Beijing, China); Kaigui Bian and Lingyang Song (Peking University, China)

1

Confronted with the severe indoor air pollution nowadays, we propose the usage of multiple robots to detect the indoor air quality (IAQ) cooperatively for fewer sensors and larger sensing area. To acquire the complete real-time IAQ distribution map, we exploit the real statistical data to construct the IAQ data model and adopt Kalman Filter to obtain the estimation of the unmeasured area. Since the movement of the robots affects the estimation accuracy, a proper movement strategy should be planned to minimize the total estimation error. To solve this optimization problem, we design a deep Q-learning approach, which provides sub-optimal movement strategies for real-time robot sensing. By simulations, we verify the adopted IAQ data model and testify the effectiveness of the proposed solution. For application considerations, we have deployed this system in Peking University since Dec. 2018 and developed a website to visualize the IAQ distribution.

Learning Cooperation Schemes for Mobile Edge Computing Empowered Internet of Vehicles

Jiayu Cao, Ke Zhang, Fan Wu and Supeng Leng (University of Electronic Science and Technology of China, China)

0

Intelligent Transportation System has emerged as a promising paradigm providing efficient traffic management while enabling innovative transport services. The implementation of ITS always demands intensive computation processing under strict delay constraints. Machine Learning empowered Mobile Edge Computing (MEC), which brings intelligent computing service to the proximity of smart vehicles, is a potential approach to meet the processing demands. However, directly offloading and calculating these computation tasks in MEC servers may seriously impair the privacy of end users. To address this problem, we leverage federated learning in MEC empowered internet of vehicles to protect task data privacy. Moreover, we propose optimized learning cooperation schemes, which adaptively take smart vehicles and road side units to act as learning agents, and significantly reduce the learning costs in task execution. Numerical results demonstrate the effectiveness of our schemes.

Distributed Deep Reinforcement Learning with Wideband Sensing for Dynamic Spectrum Access

Umuralp Kaytaz (Koc University, Turkey); Seyhan Ucar (Toyota Motor North America R&D, InfoTech Labs, USA); Baris Akgun and Sinem Coleri (Koc University, Turkey)

4

Dynamic Spectrum Access (DSA) improves spectrum utilization by allowing secondary users (SUs) to opportunistically access temporary idle periods in the primary user (PU) channels. Previous studies on utility maximizing spectrum access strategies mostly require complete network state information, therefore, may not be practical. Model-free reinforcement learning (RL) based methods, such as Q-learning, on the other hand, are promising adaptive solutions that do not require complete network information. In this paper, we tackle this research dilemma and propose deep Q-learning originated spectrum access (DQLS) based decentralized and centralized channel selection methods for network utility maximization, namely DEcentralized Spectrum Allocation (DESA) and Centralized Spectrum Allocation (CSA), respectively. Actions that are generated through centralized deep Q-network (DQN) are utilized in CSA whereas the DESA adopts a non-cooperative approach in spectrum decisions. We use extensive simulations to investigate spectrum utilization of our proposed methods for varying primary and secondary network sizes. Our findings demonstrate that proposed schemes outperform model-based RL and traditional approaches, including slotted-Aloha and Whittle index policy, while %87 of optimal channel access is achieved.

Session Chair

Jeongho Kwak (DGIST, Korea)

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Session T3-S4

Localization

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Wimage: Crowd Sensing based Heterogeneous Information Fusion for Indoor Localization

Fangmin Li (Shenzhen Institutes of Advanced Technology Chinese Academy of Science, China); Yubin Zhao (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China); Xiaofan Li (The State Radio Monitoring Center and Testing Center, China); Cheng-Zhong Xu (University of Macau, China)

0

Crowd sensing is an efficient way to collect heterogeneous information in the complicated infrastructures for fingerprinting based indoor localization. However, the information related to the dynamic trajectory are difficult to fuse due to the reliability issues from different devices and user moving habits. In this paper, we proposed a crowd sensing based indoor localization system with heterogeneous information fusion, which is called Wimage. Wimage can efficiently fuse multiple information sources related to location information, e.g., visual image, WiFi and geomagnetic data, even if the targets are moving with different and variable speeds. Then we design image-base subregion matching algorithm to locate the initial position and segmented weighted K-nearest neighbor algorithm to attain the matched trajectories in the database. A dynamic temporal warping algorithm is proposed for further calibrating the estimations. The experimental results indicate that with the helps from different kinds of information, the root mean square error is only below 0.4m, which is highly accurate for locating a target in a large scale of indoor environment.

Flight Path Optimization for UAVs to Provide Location Service to Ground Targets

Youpeng Wang and Xiaojun Zhu (Nanjing University of Aeronautics and Astronautics, China); Lijie Xu (Nanjing University of Posts and Telecommunications, China)

1

We consider using UAVs to provide location service to ground targets. UAVs fly over the target area and broadcast their locations periodically via wireless signals. When a UAV flies along a straight line, a target will observe the strongest signal when the UAV is at the closest location, based on which it can infer its location. The problem is to minimize the flight length subject to the constraint that the transmission range of a UAV is limited and all targets in the area should be located. We formulate the problem and give an optimal flight path to cover a square with side length no greater than a certain value. In the general case when the given area cannot be covered by the square, we propose to divide UAVs into two groups, whose flight paths are either parallel or orthogonal. We conduct simulations to compare our approach with existing approaches. Results verify the superiority of our approach in terms of flight length and localization error.

Cooperative Localization in Wireless Sensor Networks with AOA Ranging Measurements

Xianbo Jiang (Beijing University of Posts and Telecommunications, China); Shengchu Wang (Beijing University Of Posts And Telecommunications, China)

0

This paper researches the cooperative localization in wireless sensor networks (WSNs) with 2π/π -periodic angle-of-arrival (AOA) ranging measurements. When the orientation angles of the antenna arrays at WSN nodes are known, a ranging link loss is defined based on the tan-relationships between AOA observations and æy-minus coordinates of two neighboring nodes. Subsequently, the positioning problem under 2π -periodic AOAs is converted as a convex optimization problem about minimizing total ranging-link loss through optimizing agent positions, which is resolved by the gradient-descent (GD) method. Under π-periodic AOAs, additional 0/1 integers are introduced to indicate the front-or-back impinging directions. By relaxing 0/1 integers as continuous variables within [0,1], the positioning problem is relaxed as a nonconvex optimization one about minimizing total link loss over the agent positions and indicating variables, which is solved by the projected GD (PGD) method. Finally, Type-I least-square (LS) localizer is developed for WSNs with both 2π and π-periodic AOAs. When the orientation angles are unknown, Type-II LS localizer is developed by combining Type-I LS localizer with a maximum-likelihood (ML) orientation estimator, which alternatively updates agent positions and orientation angles. Simulation results validate that the proposed LS-type localizers outperform existing localizers.

Depthwise Separable Convolution based Passive Indoor Localization using CSI Fingerprint

Wenjing Xun, Lijuan Sun, Chong Han, Zhaoxiao Lin and Jian Guo (Nanjing University of Posts and Telecommunications, China)

0

Wi-Fi-based indoor localization has received extensive attention in the academic community. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, we propose a depthwise separable convolution based passive indoor localization system (DSCP) using Wi-Fi channel state information (CSI). DSCP is a fingerprint-based localization system, which includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then amplitude differences of these CSI subcarriers are extracted for constructing location fingerprints, thereby training the CNN. In the online localization phase, CSI data is first collected at the test locations, then the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, upper than 97%, and a small median localization distance error of 0.98 m in open indoor scenarios.

Packet Corruption Tolerant Localization for Underwater Acoustic Sensor Networks

Keyong Hu, Xianglin Song and Zhongwei Sun (Qingdao University of Technology, China); Hanjiang Luo (Shandong University of Science and Technology, China); Zhongwen Guo (Ocean University of China, China)

0

Existing range-based localization schemes for underwater acoustic sensor networks (UASNs) rely on sufficient and accurate distance measurements. However, in practice, ranging packets are inevitably corrupted due to packet collisions and signal noises, resulting in missing and noisy distance measurements and further degrading localization performance significantly. In this paper, we propose a packet corruption tolerant localization algorithm to address this challenge. First, we design an energy-efficient mechanism to gather inter-node distance measurements and form partially observed Square Distance Matrix (SDM). Then, leveraging the intrinsic low-rank structure of SDM, the reconstruction of true SDM is formulated as a Frobenius-norm regularized matrix factorization problem and an improved Newton-Raphson method is designed to solve this problem. Finally, we apply Multi-Dimension Scaling technique to localize all the nodes based on the reconstructed SDM. Simulation results demonstrate that, our proposed algorithm outperforms the benchmark approaches in terms of localization accuracy, coverage and stability.

On the Localization of Unmanned Aerial Vehicles with Cellular Networks

Irshad A. Meer, Mustafa Ozger and Cicek Cavdar (KTH Royal Institute of Technology, Sweden)

1

Localization plays a key role for safe operation of UAVs enabling beyond visual line of sight applications. Compared to GPS based localization, cellular networks can reduce the positioning error and cost since cellular connectivity is becoming a prominent solution as a communication system for UAVs. As a first step towards localization, UAV needs to receive sufficient number of localization signals each having a signal to interference plus noise ratio (SINR) greater than a threshold. On the other hand, three-dimensional mobility of UAVs, altitude dependent channel characteristics between base stations (BSs) and UAVs, its line of sight and non-line of sight conditions, and resulting interference from the neighboring BSs pose challenges to receive usable signals from the required number of BSs. In this paper, we utilize a tractable approach to calculate localizability probability, which is defined as the probability of successfully receiving usable signals from at least a certain number of BSs. Localizability has an impact on overall localization performance regardless of the localization technique to be used. In our simulation study, we investigate the relation between the localizability probability with respect to the number of participating BSs, post-processing SINR requirement, air-to-ground channel characteristics, and network coordination, which are shown to be the most important factors for the localizability performance of UAVs. We observe the localizability performance is better at higher altitudes which indicates that localizability with cellular networks for UAVs is more favorable than for terrestrial users.

Session Chair

Jiguang He (University of Oulu, Finland)

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Session T3-S5

Energy Efficiency

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

Energy and Spectral Efficiency Tradeoff in OFDMA Networks via Antenna Selection Strategy

Ata Khalili (Amirkabir University of Technology, Iran); Derrick Wing Kwan Ng (University of New South Wales, Australia)

1

In this paper, we investigate the joint resource allocation and antenna selection algorithm design for uplink orthogonal frequency division multiple access (OFDMA) communication system. We propose a multi-objective optimization framework to strike a balance between spectral efficiency (SE) and energy efficiency (EE). The resource allocation design is formulated as a multi-objective optimization problem (MOOP), where the conflicting objective functions are linearly combined into a single objective function employing the weighted sum method. In order to develop an efficient solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as a lower bound of the objective function. Then an iterative suboptimal algorithm is proposed to maximize the approximate objective function. Numerical results unveil an interesting tradeoff between the considered conflicting system design objectives and reveal the improved EE and SE facilitated by the proposed transmit antenna selection in OFDMA systems.

Heterogeneity-based Energy-efficient Transmission in Dense Small Cell Networks

Shie Wu (Yantai University, P.R. China); Rui Yin (School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, China); Ningfei Dong (Yantai University, China); Xia Liu (Yantai University, P.R. China)

0

Green communications in dense small cell network (DSCNs) has attracted much attention. Energy saving (ES) and energy efficiency (EE) are two main goals and they are usually optimized separately. In this paper, taking into account the heterogeneity and cooperation opportunities among small cells, EE and ES are jointly optimized through subframe configuration and power allocation in the DSCNs. To quantize the heterogeneity, we define an EE preference function. Accordingly, a multi-objective optimization problem is formulated while considering the EE and ES optimization simultaneously. Due to the coupling of EE and ES, obtaining the solution is non-trivial. A heterogeneity-based ES and EE (HESEE) optimization algorithm is proposed, where the sleep mechanism is adopted via the subframe configuration. Particularly, the concave-convex procedure (CCCP) method is applied to solve the non-concave sum-of-ratios optimization for system EE. Simulation results show that the proposed HESEE algorithm can optimize the EE of small cell base stations (SBSs) according to their EE preference weights. In addition, compared with the base scheme, the HESEE algorithm can save energy by over 35.2% while improving system EE by up to 23%.

Dynamic Load Adjustments for Small Cells in Heterogeneous Ultra-dense Networks

Qi Zhang (Beijing University of Posts and Telecommunications, China); Xiaodong Xu (Beijing University of Posts and Telecommunications & Wireless Technology Innovation Institute, China); Jingxuan Zhang and Xiaofeng Tao (Beijing University of Posts and Telecommunications, China); Cong Liu (China Mobile Research Institute, China)

1

The ultra-dense deployment of small cells has been applied to the 5th-generation (5G) mobile networks. A large number of base stations (BSs) will lead to a dramatic increase in energy consumption, and network resources will be more difficult to fully utilize. In this paper, we propose the dynamic load adjustments (DLA) algorithm for small cells in heterogeneous ultra-dense networks. The proposed algorithm applies Q-learning to learn effective offloading policies which could combine the energy-saving function and the load balancing function. Based on the DLA algorithm, the heterogeneous ultra-dense networks could adjust the traffic load to turn off some redundant BSs or balance the load between heavily loaded BSs and lightly loaded BSs. The simulation results show that the algorithm not only improves the network energy efficiency when the average load of the networks is light, but also improves the network throughput when the average load of the networks is heavy.

Energy Efficient Joint Resource Allocation and Clustering Algorithm for M2M Communication Systems

Changzhu Liu, Rong Chai and Ahmad Zubair (Chongqing University of Posts and Telecommunications, China); Qianbin Chen (Chongqing University of Posts and Telecommunication, China)

1

In recent years, machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. In M2M communication systems, machine type communication devices (MTCDs) are capable of communicating with each other intelligently under highly reduced human interventions. Although diverse types of services are expected to be supported for MTCDs, various quality of service (QoS) requirements and network states pose difficulties and challenges to the resource allocation and clustering schemes of M2M communication systems. In this paper, we address the joint resource allocation and clustering problem in M2M communication systems. To achieve the efficient resource management of the MTCDs, we propose a joint resource management architecture, and design a joint resource allocation and clustering algorithm. More specifically, by defining system energy efficiency as the sum of the energy efficiency of the MTCDs, the joint resource allocation and clustering problem is formulated as an energy efficiency maximization problem. As the original optimization problem is a nonlinear fractional programming problem, which cannot be solved conveniently, we transform the optimization problem into power allocation subproblem and clustering subproblem. Applying iterative method-based energy efficiency maximization algorithm, we first obtain the optimal power allocation strategy based on which, we then propose a modified K-means algorithm to obtain the clustering strategy. Numerical results demonstrate the effectiveness of the proposed algorithm.

Energy Efficient Bidirectional Relaying Network Coded HARQ Transmission Scheme for S-IoT

Zilin Ni (Harbin Institute of Technology, China); Jian Jiao (Harbin Institute of Technology - Shenzhen, China); Shiqi Liu (Harbin Institute of Technology (Shenzhen), China); Shaohua Wu (Harbin Institute of Technology, China); Qinyu Zhang (Shenzhen Graduate School, Harbin Institute of Technology, China)

0

Recently, with the development of the next generation of high throughput satellites, deploying satellite-based Internet of Things (S-IoT) is suggested to solve the increasing demand for ubiquitous broadband access capability terrestrial communications. Under the current situation that the number of communication devices and the hardware capabilities of devices continue to increase, network coding becomes an effective way to further improve the throughput and efficiency in S-IoTs. In this paper, a Network Coded Hybrid Automatic Repeat Request (NCed HARQ) transmission scheme is proposed based on typical bidirectional relaying scenarios of S-IoT, and a general process of the NCed HARQ is presented. The corresponding detailed transmission process is given, and the theoretical performance index is derived and verified by simulations, which emphasizes the benefit of network coding. Besides, we adopt matrix exponential distribution in the calculation to make formulations more concise and unified.

Session Chair

Rong Chai (Chongqing University of Posts and Telecommunications, China)

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Session T3-S6

Routing

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

A Three-level Routing Hierarchy in improved SDN-MEC-VANET Architecture

Xuefeng Ji, Wenquan Xu, Chuwen Zhang and Bin Liu (Tsinghua University, China)

1

Existing routing algorithms that based on traditional Vehicular ad-hoc NETwork (VANET) architectures cannot provide fast and diverse routing services due to dynamic and unstable environment. To address this issue, we propose a three- level routing hierarchy in improved Software-Defined VANET architecture based on Mobile Edge Computing (SDN-MEC- VANET) to improve routing performance and enrich the data transmission mode for the VANET. Moreover, it can be applied to almost all VANET protocols, enabling protocol-independent forwarding. Besides, this improved architecture can coordinate different edge devices to timely adjust the service delivery strategy under the predictive correction from controllers, providing high-bandwidth and low-delay transmission for Internet of Vehicles (IoV). Meanwhile, MEC technology is introduced to perform local control, leveraging the storage and computing capabilities of edge devices to reduce the processing pressure of the controller. Simulation results show that our routing algorithm in improved network architecture can achieve a higher packet delivery ratio within a reasonable delay than other approaches under different scenarios of network scale, communication frequency and vehicular velocity.

NIHR: Name/ID Hybrid Routing in Information-Centric VANET

Wenquan Xu, Xuefeng Ji, Chuwen Zhang and Bin Liu (Tsinghua University, China)

0

Vehicular Ad hoc network (VANET) has received great attention in recent research, but many challenges still lie in innovating efficient routing protocols to support the highly dynamic environment. Existing ID-based routing protocols cannot fundamentally tackle the dynamic topology problem in VANET. The recent emerging Information-Centric Networking (ICN) makes routing decisions based on data itself instead of a particular host, seeming to have the potential to handle the dynamic topology, but problems (e.g., severe flooding overhead) still remain. Therefore, inspired by the idea of ICN, we propose a name/ID hybrid routing (NIHR) protocol that combines the data-name- based routing and host-ID-based routing to address the above two issues simultaneously. In particular, we develop an announce strategy to improve the efficiency of the in-network cache, and we design a bloom filter based structure to achieve fast content lookup. Simulation results show NIHR's high performance in terms of Packet Delivery Ratio (PDR), Roundtrip time (RTT) and roundtrip hop count. Especially, to verify NIHR's performance in real-world scenarios, we have implemented a vehicular real-time video conference system based on MK5 OBU [1] (On-Board Unit).

FNTAR: A Future Network Topology-aware Routing Protocol in UAV Networks

Jianfei Peng (Nanjing University of Aeronautics and Astronautics, China); Hang Gao and Liang Liu (Colleage of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China); Yuting Wu (Nanjing University of Aeronautics and Astronautics, China); Xiangyu Xu (Nanjing University Of Aeronautics And Astronautics, China)

0

Unmanned aerial vehicles (UAVs) can gather data in the air and transmit the data to the ground station. Multi- UAV systems have been used in an increasing number of mission scenarios and routing protocols play a critical role in UAV network communications. It is now well established that unstable link quality and frequently changing network topology pose significant challenges for messages forwarding in UAV networks. Hence, traditional mobile ad-hoc network routing protocols do not fit well in UAV networks. In many UAV applications, the flight paths of UAVs are planned in advance before performing missions. The positions and motion information of UAVs are available through Global Positioning System (GPS) and inertial sensors, which can be utilized to calculate the future positions of UAVs. Therefore, the future topology of the UAV network is also available. However, existing work does not take advantage of this information. Based on the trajectory, location and motion information of the UAVs, this paper proposes a future network topology-aware routing (FNTAR) protocol, which uses future location information to make superior routing decisions. Moreover, to mitigate data loss problems caused by unstable links and highly dynamic topology, FNTAR can forward messages to multiple excellent next-hop UAVs based on future network topology, and these UAVs can deliver messages to destinations faster. We implement FNTAR in the simulation experiment, the simulation results demonstrate that FNTAR can achieve lower latency and higher delivery ratio than DTNgeo protocol.

Exploiting Mobile Contact Patterns for Message Forwarding in Mobile Opportunistic Networks

Mohd Yaseen Mir and Chih-Lin Hu (National Central University, Taiwan)

1

When sparse and challenged networks are characterized by small node population, non-randomized node distribution, and low node density, it is hard to maintain end-to- end connectivity among mobile nodes. The mobile opportunistic networking technology provides data dissemination services by means of transient inter-node communications during node movements in a network. Recent studies intend to exploit node mobility to store, carry and forward data upon opportunistic contacts among nodes. To address the uncertainty nature of future contacts, traditional ways replicate message copies to increase data delivery. However, most replication techniques forward message copies either in a greedy manner or select the next forwarding node by considering only contact history. In this paper, we exploit contact patterns among mobile nodes and propose a Regular and Sporadic Contact-Based Routing (RSCR) scheme where regular and sporadic contact patterns are defined to distinguish contacts of which either periodically or occasionally appear in a time scale, to enhance delivery rate in a cost- effective manner. We conduct synthetic simulation to examine the RSCR scheme under the SLAW and Infocom'05 mobility traces. Performance results show its efficiency on the successful delivery rate with lower message overhead as compared with the typical Epidemic and PRoPHETv2 schemes.

Reliable and Power Confined Routing in Large and Densely Deployed 6TiSCH Mesh Networks

Yichao Jin (Toshiba Research Europe Ltd, United Kingdom (Great Britain)); Michael Baddeley (Toshiba Research Europe Ltd., United Kingdom (Great Britain)); Usman Raza (Toshiba Research Europe Limited, United Kingdom (Great Britain)); Aleksandar Stanoev (Toshiba Research Europe Ltd, United Kingdom (Great Britain)); Mahesh Sooriyabandara (Toshiba Research Europe Limited, United Kingdom (Great Britain))

0

RPL, the de-facto standard for low power and lossy networks, forms multi-hop routing structures between network nodes and a single root. It intrinsically minimises the number of hops across the mesh, however this can result in large distances between adjacent forwarding nodes. Consequently, during forwarding operations, the received signal strength can be close to the receiver sensitivity threshold and result in frequent packet losses due to link fluctuations. RECLAIM, our proposed approach, overcomes this challenge by transmitting routebuilding messages at a reduced power level at first while switching back to the normal transmission power level during the actual data communication phase. This results in more reliable links per hop and ensures link budget above the receiver sensitivity threshold. This however creates more hops and a longer routing path in order to reach destination. Hence, RECLAIM further applies efficient and non-conflicting 6TiSCH scheduling method to coordinate those communication events in a non conflicting manner which do not collide or cause interference to each other, resolving the issue that has traditionally prevented this approach. Our detailed simulation shows that RECLAIM drops 60 times less packets than standard RPL, and achieves 99.9999% packet delivery ratio.

Session Chair

Yichao Jin (Toshiba Research Europe Ltd, United Kingdom)

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Session T4-S3

Localization and Tracking

Conference

2:00 PM — 3:30 PM KST

Local

May 26 Tue, 1:00 AM — 2:30 AM EDT

NLOS-Aware VLC-based Indoor Localization: Algorithm Design and Experimental Validation

Chuanxi Huang (Institut Supérieur d'Electronique de Paris (ISEP), France); Xun Zhang (Institut Superieur d Electronique de Paris, France); Fen Zhou (Institut Supérieur d'Electronique de Paris (ISEP) & University of Avignon, France); Zhan Wang and Lina Shi (ISEP, France)

1

The Visible Light Indoor Positioning System (VL-IPS) has been a popular research area recently. In VL-IPS, many localization methods have been proposed by leveraging the Received Signal Strength (RSS) based trilateration. However, the traditional RSS based trilateration localization (RSS-TL) method is very sensitive to the lighting environment and would results in a big localization error due to the presence of non- line-of-sight (NLOS) light signal. In light of this, we propose a novel NLOS-aware localization algorithm, namely Enhanced Fingerprinting-aided RSS-TL (EFP-RSS-TL). It permits to improve the localization accuracy for the corner regions of a room by eliminating the NLOS impact while keeping the same high accuracy for the room center. This is achieved by leveraging a RSS fingerprint database which records the line-of-sight (LOS) light power ratios beforehand. For validation purpose, we built a real VL-IPS platform and implemented the proposed algorithm. Experimental results show that the proposed NLOS-aware EFP- RSS-TL algorithm enables to reduce significantly the average positioning error (by up to 79%) compared to its counterparts. Besides, our proposal cuts the database size by 50% and is more robust to environment changes. In a room of 4.7 m x 2.7 m, the achieved average positioning error is around 6 cm when it is vacant and it is no more than 14.5 cm when it is occupied by several people.

ROLATIN: Robust Localization and Tracking for Indoor Navigation of Drones

Alireza Famili and Jung-Min (Jerry) Park (Virginia Tech, USA)

0

In many drone applications, drones need the ability to fly fully or partially autonomously to carry out their mission. To enable such fully/partially autonomous flights, the ground control station that is supporting the drone's operation needs to constantly localize and track the drone, and send this information to the drone's navigation controller to enable autonomous/semi- autonomous navigation. In outdoor environments, localization and tracking can be readily carried out using GPS and the drone's Inertial Measurement Units (IMUs). However, in indoor areas or GPS-denied environments, such an approach is not feasible. In this paper, we propose a localization and tracking scheme for drones called ROLATIN (Robust Localization and Tracking for Indoor Navigation of drones) that was specifically devised for GPS-denied environments. Instead of GPS signals, ROLATIN relies on speakergenerated ultrasonic acoustic signals to estimate the target drone's location and track its movement. Compared to vision and RF signal-based methods, our scheme offers a number of advantages in terms of performance and cost.

Indoor Localization with Particle Filter in Multiple Motion Patterns

Qiao Li, Xuewen Liao and Zhenzhen Gao (Xi'an Jiaotong University, China)

0

In this paper, a novel mobile tracking method based on pedestrian dead reckoning (PDR) and wireless local area network (WLAN) RSS fingerprint is proposed, which estimates the real-time location of pedestrian continuously using the improved particle filter. The existing PDR systems mostly focus on the condition that sensor axes are relatively fixed to user. However, the sensor axes may be changing during walking period in several motion patterns, for example that the smartphone is swinging with hand or kept in bag. Therefore, we propose a novel PDR algorithm for different handheld patterns, which detects the steps based on multimode finite-state machine (MFSM) with adaptive updating thresholds and estimates the heading direction with principal component analysis (PCA) and ambiguity resolution. On the other hand, for a long continuous walking process, localization error will accumulate and lead to the particle filter losing tracking of the target device. To deal with this problem, we design an improved particle filter with advanced resampling strategy for recovery when localization fails. We conduct experiments for four motion patterns in two realistic representative indoor environments: office building and shopping mall. Experiment results reveal the proposed localization system could achieve an average localization accuracy within 2 m even in the toughest motion pattern.

An Enhanced Direction Calibration Based on Reinforcement Learning for Indoor Localization System

Qiao Li, Xuewen Liao and Zhenzhen Gao (Xi'an Jiaotong University, China)

0

In this paper, we propose an advanced direction calibration method for the smartphone-based indoor localization system on the basis of map information and reinforcement learning (RL). Currently, the direction estimated by pedestrian dead reckoning (PDR) is biased due to the low-precision sensor in smartphone and magnetic field distortion in indoor environment. Thus, the direction calibration methods draw increasing attention. Since the movement of pedestrian is restricted by the indoor environment, the map information could be used to correct the heading of pedestrian and then improve the localization performance. Furthermore, since the tracking of pedestrian can be modeled as a Markov decision process. we propose a novel direction calibration algorithm based on deep Q-network (DQN). Different from the traditional direction calibration algorithms that usually rely on image processing, the proposed method use DQN to find an optimal policy to determine the moving direction. We conduct experiments in a realistic representative office environment to reveal the validity of the proposed direction calibration algorithm. The experiment results indicate that the proposed algorithm can remarkably alleviate the cumulative error, and improve the accuracy, stability and robustness of the indoor positioning system.

TLS-Regularization Framework for Target Tracking under Perturbations

Mostafizur Laskar (Indian Institute of Technology Kharagpur, India); Debarati Sen (Indian Instutute of Technology Kharagpur, India)

0

Perturbation in discrete dynamic systems (e.g., a manoeuvring aircraft, an autonomous vehicle having frequent lane change or turning, etc.) makes the linear tracking filter(e.g., Kalman Filter (KF)) sub-optimal. The existing robust filters such as Ridge-based KF (Ridge-KF), Tikhonov-based KF (TRKF), and minimax algorithm perform enhanced estimation under low and moderate perturbations only. The investigation of severe perturbation for multi-degree of freedom (m-DOF) motion of manoeuvres is carried out in this research article. The severity of perturbation due to m-DOF motion makes the observation matrix ill-posed for the tracking filters. To enhance the estimation performance (in terms of root means square error (RMSE)), we propose a novel algorithm called TLSKF. It offers much-improved performance than KF, Ridge-KF, and TRKF under high perturbation for estimating state parameters (position, velocity, etc.). Further, TLSKF approaches to KF at extremely low perturbation. The improvement in the RMSE enhances the tracking resolution of the radar. The comparison with existing literature is also strived to justify the novelty of the proposed algorithm.

Session Chair

Jin-Ho Chung (Ulsan National Institute of Science and Technology, Korea)

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Session T1-S10

Multiple Access and Interference Management

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Multiplexing More Data Streams in the MU-MISO Downlink by Interference Exploitation Precoding

Ang Li (Xi'an Jiaotong University, China); Christos Masouros (University College London, United Kingdom (Great Britain)); Xuewen Liao (Xi'an Jiaotong University, China); Yonghui Li and Branka Vucetic (University of Sydney, Australia)

1

In this paper, we focus on the constructive interference (CI) precoding for the scenario when the number of streams simultaneously transmitted by the base station (BS) is larger than that of transmit antennas at the BS, and derive the optimal precoding structure by employing the pseudo inverse. We show that the optimal pre-scaling vector in IE precoding is equal to a linear combination of the right singular vectors that correspond to zero singular values of the coefficient matrix. By formulating the dual problem, we further show that the optimal precoding matrix can be expressed as a function of the dual variables in a closed form, and an equivalent quadratic programming (QP) formulation is derived for computational complexity reduction. Numerical results validate our analysis and demonstrate significant performance improvements for interference exploitation precoding in the considered scenario.

Intelligent Reflecting Surface Assisted Non-Orthogonal Multiple Access

Gang Yang, Xinyue Xu and Ying-Chang Liang (University of Electronic Science and Technology of China, China)

3

Intelligent reflecting surface (IRS) is a new and disruptive technology to achieve spectrum-, energy, and cost- efficient wireless networks. In this paper, we consider an IRS-assisted non-orthogonal-multiple-access (NOMA) system in which a base station (BS) transmits superposed downlink signals to multiple users. A combined-channel-strength (CCS) based user ordering scheme is first proposed. In order to optimize the rate performance and ensure user fairness, we further maximize the minimum decoding signal- to-interference-plus-noise-ratio (i.e., equivalently the rate) of all users, by jointly optimizing the power allocation at the BS and the phase shifts at the IRS. However, the formulated problem is non-convex and difficult to be solved optimally. By leveraging the block coordinate descent and semidefinite relaxation techniques, an efficient algorithm is then proposed to obtain a suboptimal solution. Simulation results show that the IRS-assisted downlink NOMA system can enhance the rate performance significantly, compared to traditional NOMA without IRS and traditional orthogonal multiple access with/without IRS, and the rate degradation due to the IRS's finite phase resolution is slight.

Bit Error Probability for Asynchronous Channel Access with Interference Cancellation and FBMC

Maxim Penner and Sami Akın (Leibniz Universität Hannover, Germany); Martin Fuhrwerk (Baker Hughes, Germany); Jürgen Peissig (Leibniz Universität Hannover, Germany)

2

Future wireless communication standards will include technologies to provide access to an increasing number of users, for example Machine-Type Communication (MTC), which is expected to interconnect billions of devices. Managing such a large number of network participants in centrally coordinated systems suffers from large controlling overhead as each device needs to be assigned resources and maintain synchronization. In this paper, we investigate systems with asynchronous channel access, in which signals are transmitted without prior resource coordination. In such uncoordinated networks, signal collisions are inevitable and pose a major challenge for system design. We present a closed-form solution for the Bit Error Probability (BEP) of colliding signals modulated with Filter Bank Multicarrier (FBMC), a modern multicarrier scheme that allows a flexible signal design. We additionally derive a solution for the BEP when Successive Interference Cancellation (SIC) is applied, a scheme where successfully decoded signals are removed from a collision in order to improve decoding of other signals implicated in the collision. The results are valid for any numbers of colliding FBMC signals over a broad range of doubly-selective channel configurations. Furthermore, we provide an overview of when interference cancellation is beneficial depending on the power ratio between colliding signals and the selected channel models.

Full Duplex Based Digital Out-of-Band Interference Cancellation for Collocated Radios

Thomas Ranström (Swedish Defense Research Agency, Sweden); Erik Axell (Swedish Defence Research Agency, Sweden)

2

In this work, a novel out-of-band interference cancellation (OOBIC) method, based on a full duplex interference cancellation technique, is proposed. The aim of the OOBIC is to counteract the interference that may occur when collocated radios transmit and receive in closely located frequency bands. The main advantage of the method is that the cancellation can be performed directly on the data signal, with the desired signal present, without complex nonlinear modeling, and without additional synchronization between the collocated radios. The method is validated both analytically and through simulations, and is shown to successfully increase the signal quality in OOB interference situations where the signal otherwise would have been unrecoverable.

Adaptive Threshold Detection and ISI Mitigation in Mobile Molecular Communication

Amit Kumar Shrivastava and Debanjan Das (International Institute of Information Technology, Naya Raipur, India); Rajarshi Mahapatra (IIIT Naya Raipur, India)

2

Due to the dynamic nature of nanomachines and diffusion channel, detection is challenging in mobile molecular communication (MMC). In general, signal detection is performed on the number of received molecules with respect to a fixed threshold. However, in MMC number of received molecules varies in each bit interval, which makes detection very challenging if a fixed threshold is considered. In addition to this, the dynamic nature of nanomachines, communication channel impacted intersymbol interference (ISI), and makes it time-varying. In this work, we propose a detection technique with adaptive threshold for signal detection in MMC. The selection of adaptive threshold depends on the parameters such as the dynamic distance between nanomachines, diffusion coefficient etc. Detection using adaptive threshold improves the detection performance and also mitigates ISI manifold. In this paper, we use modified concentration shift keying (M-CSK) based modulation to release more amount of signaling molecules for bit-1 than bit-0. Received signal is reconstructed in each bit interval using the average distance variation in a bit interval and the reconstructed received signal is subtracted from the total received signal in subsequent bit duration. Detection threshold is calculated by applying maximum a posteriori (MAP) rule to reconstructed signals for the bit-1 and the bit-0 of previous bit interval. Performance of this detector is compared with an existing detection technique. Results reveal a better bit error rate (BER) performance in terms of parameters like coherence time of the channel (zero BER for coherence time of two bit durations), increasing diffusion coefficient and initial distance between nanomachines.

Session Chair

Sang-Woon Jeon (Hanyang University, Korea (South))

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Session T1-S8

Low Latency Communications

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Cross-Link Interference Suppression By Orthogonal Projector For 5G Dynamic TDD URLLC Systems

Ali Esswie (Nokia Bell Labs, Denmark); Klaus Pedersen (Nokia - Bell Labs, Denmark)

1

Dynamic time division duplexing (TDD) is envisioned as a vital transmission technology of the 5G new radio, due to its reciprocal propagation characteristics. However, the potential cross-link interference (CLI) imposes a fundamental limitation against the feasibility of the ultra-reliable and low latency communications (URLLC) in dynamic-TDD systems. In this work, we propose a near-optimal and complexity-efficient CLI suppression scheme using orthogonal spatial projection, while the signaling overhead is limited to B-bit, over the back-haul links. Compared to the state-of-the-art dynamic-TDD studies, proposed solution offers a significant improvement of the URLLC outage latency, e.g., ~ —199% reduction, while boosting the achievable capacity per the URLLC packet by ~ +156%.

Robust Integration of Computation and Communication in B5G Cellular Internet of Things

Qiao Qi (ZheJiang University, China); Xiaoming Chen, Caijun Zhong and Zhaoyang Zhang (Zhejiang University, China)

0

In this paper, we investigate the issue of integrated computation and communication in beyond fifth-generation (B5G) cellular internet of things (IoT) networks with massive connectivity. By exploiting the open nature of wireless channels, a comprehensive deign framework integrating computation and communication over the same spectrum is first put forward for massive IoT. To achieve efficient integration of computation and communication under practical but adverse conditions, a robust algorithm is proposed by jointly optimizing transmit power and receive beamforming, with the goal of minimizing the computation error of computation signals while guaranteeing the requirement of communication signals. Finally, extensive simulations validate the robustness and effectiveness of the proposed algorithm for B5G cellular IoT.

Dynamic HARQ with Guaranteed Delay

Mahyar Shirvanimoghaddam (University of Sydney, Australia); Hossein Khayami (Sharif University of Technology, Iran); Yonghui Li and Branka Vucetic (University of Sydney, Australia)

1

In this paper, a dynamic-hybrid automatic repeat request (D-HARQ) scheme with guaranteed delay performance is proposed. As opposed to the conventional HARQ that the maximum number of re-transmissions, L, is fixed, in the proposed scheme packets can be re-transmitted more times given that the previous packet was received with less than L re-transmissions. The dynamic of the proposed scheme is analyzed using the Markov model. For delay sensitive applications, the proposed scheme shows a superior performance in terms of packet error rate compared with the conventional HARQ and Fixed retransmission schemes when the channel state information is not available at the transmitter. We further show that D-HARQ achieves a higher throughput compared with the conventional HARQ and fixed re-transmission schemes under the same reliability constraint.

On Error Rate Analysis for URLLC over Multiple Fading Channels

Jinho Choi (Deakin University, Australia)

0

In this paper, we study ultra-reliable and low-latency communication (URLLC) under fading using repetition diversity through multiple orthogonal radio resource blocks (e.g., multiple frequency or time bins). We investigate an approach to find an upper-bound on the packet error rate when a finite-length code is used. From simulation results, we find that the bound is reasonably tight for wide ranges of signal-to-noise ratio (SNR) and the number of multiple bins. Thus, the derived bound can be used to determine key parameters to guarantee the performance of URLLC in terms of the packet error rate.

Fast Cross Layer Authentication Scheme For Dynamic Wireless Network

ZhiYuan Zhang, Na Li, Shida Xia and Xiaofeng Tao (Beijing University of Posts and Telecommunications, China)

0

Current physical layer authentication (PLA) mechanisms are mostly designed for static communications, and the accuracy degrades significantly when used in dynamic scenarios, where the network environments and wireless channels change frequently. To improve the authentication performance, it is necessary to update the hypothesis test models and parameters in time, which however brings high computational complexity and authentication delay. In this paper, we propose a lightweight cross-layer authentication scheme for dynamic communication scenarios. We use multiple characteristics based PLA to guarantee the reliability and accuracy of authentication, and propose an upper layer assisted method to ensure the performance stability. Specifically, upper layer authentication (ULA) helps to update the PLA models and parameters. By properly choosing the period of triggering ULA, a balance between complexity and performance can be easily obtained. Simulation results show that our scheme can achieve pretty good authentication performance with reduced complexity.

Session Chair

Jemin Lee (Daegu Gyeongbuk Institute of Science and Technology (DGIST), Korea (South))

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Session T1-S9

Deep Learning for Wireless Communications 2

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Deep Learning Based Fast Downlink Channel Reconstruction For FDD Massive MIMO Systems

Mengyuan Li and Yu Han (Southeast University, China); Chao-Kai Wen (National Sun Yat-sen University, Taiwan); Xiao Li and Shi Jin (Southeast University, China)

1

The spatial reciprocity enables the downlink channel reconstruction in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems by obtaining the frequency-independent parameters in the uplink. However, the algorithms to estimate these parameters are typically complex and time-consuming. In this paper, we regard the channel as an image and utilize you only look once (YOLO), an advanced deep learning- based object detection network, to locate the bright spots in the channel image, then the frequency-independent parameters can be estimated rapidly. Superior to the traditional algorithm that iteratively extracts the paths, YOLO can detect all the path simultaneously. Experimental results show that YOLO can greatly deplete the running time to obtain the frequency-independent parameters and reconstruct the FDD massive MIMO downlink channel with satisfactory accuracy.

Deep Learning based Low-Rank Channel Recovery for Hybrid Beamforming in Millimeter-Wave Massive MIMO

Nuan Song, Chenhui Ye and Xiaofeng Hu (Nokia Bell Labs China, China); Tao Yang (Nokia Shanghai Bell, China)

4

Massive Multiple Input Multiple Output (MIMO) at millimeter wave bands is able to boost the system throughput. A key challenge for the hybrid beam- forming design in massive MIMO systems is the acquisition of the full channel state information, since the number of radio frequency chains is much smaller than that of the antennas. Conventional methods require a longer measurement time, a large overhead, or costly signal processing efforts. Therefore, we propose an efficient and adaptable deep neural network based low-rank channel recovery scheme for a hybrid array based massive MIMO system. The proposed neural network architecture includes a common feature extraction module and the adaptable recovery module. The feature extraction, built on the convolutional neural network with residual learning functionality, can efficiently learn the essential features from the low-rank measurements. The adaptable key recovery module maps the essential features to the full channel information. The proposed architecture enables an efficient learning procedure and can be easily adapted to different cases. Simulation results are carried out and compared with existing solutions, showing the potential of applying deep learning concepts in millimeter wave massive MIMO systems.

IMNet: A Learning Based Detector for Index Modulation Aided MIMO-OFDM Systems

Jinxue Liu and Hancheng Lu (University of Science and Technology of China, China)

0

Index modulation (IM) brings the reduction of power consumption and complexity of transmitter to classical multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, due to the introduction of IM, the complexity of the detector at receiver is greatly increased. Furthermore, the detector also requires the channel state information at receiver (CSIR), which leads to high system overhead. To tackle these challenges, in this paper, we introduce deep learning (DL) in designing a non-iterative detector. Specifically, based on the structural sparsity of the transmitted signal in IM aided MIMO-OFDM (IM-MIMO-OFDM) systems, we first formulate the detection process as a sparse reconstruction problem. Then, a DL based detector called IMNet, which combines two subnets with the traditional least square method, is designed to recover the transmitted signal. To the best of our knowledge, this is the first attempt that designs the DL based detector for IM-MIMO-OFDM systems. Finally, to verify the adaptability and robustness of IMNet, simulations are carried out with consideration of correlated MIMO channels and imperfect CSIR. The simulation results demonstrate that the proposed IMNet outperforms existing detectors in terms of bit error rate and computational complexity under various scenarios.

Blind Packet-Based Receiver Chain Optimization Using Machine Learning

Mohammed Radi (TU Dresden, Germany); Emil Matus and Gerhard P. Fettweis (Technische Universität Dresden, Germany)

3

The selection of the most appropriate equalization-detection-decoding algorithms in wireless receivers is a challenging task due to the diversity of application requirements, algorithm performance-complexity trade-offs, numerous transmission modes, and channel properties. Typically, the fixed receiver- chain is employed for specific application scenario that may support iterative processing for better adaptation to variable channel conditions. We propose a novel method for optimizing receiver efficiency in the sense of maximizing packet transmission reliability while minimizing receiver processing complexity. We achieve this by packet-wise dynamic selection of the least complex receiver that enables error-free packet reception out of set of available receivers. The scheme employs convolutional neural network (CNN) and supervised deep learning approach for packet classification and subsequent prediction of the optimum receiver using raw baseband signals. The proposed scheme aims to approach a packet error rate close to the rate of the most complex receiver architecture while using a combination of both low and high complexity architectures. This is achieved by employing the neural network based classifier to dynamically select packet-specific optimum architecture; i.e. instead of using the most complex receiver for all packets, the approach dynamically assigns the packet to the most appropriate receiver in terms of equalization-detection-decoding capability and the least possible complexity. We analyze the performance of the proposed scheme considering various channel scenarios. The system demonstrates excellent packet classification performance resulting in the significant performance increase and the reduction of the usage of the functional blocks that can go up to 96% of the time in different scenarios.

Neural Network MIMO Detection for Coded Wireless Communication with Impairments

Omer Sholev (Ben Gurion University, Israel); Haim H Permuter (Ben-Gurion University, Israel); Eilam Ben-Dror (Huawei Technologies ltd., Israel); Wenliang Liang (China)

0

In this paper, a neural network based MultipleInput-Multiple-Output (MIMO) algorithm is presented. The algorithm is specifically designed to be integrated in a coded MIMO-OFDM system, and is based upon projected gradient descent iterations. We combine our model as a part of a modern coded MIMO-OFDM system, and we compare its performance with common MIMO detectors on simulated data, as well as on field data. We also investigated our model's performance in the presence of several common communication impairments, and demonstrated empirically its robustness. We show empirically that a single trained model is suited for the detection of both coded and uncoded data, with or without impairments, and in the presence of a wide range of tested SNR levels.

Session Chair

Junil Choi (KAIST, Korea (South))

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Session T2-S3

Scheduling and Radio Resource Management

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Novel QoS control framework for automotive safety-related and infotainment services

Daniel Fabian Külzer (BMW Group Research and Technology, Germany); Slawomir Stanczak (Technische Universität Berlin & Fraunhofer Heinrich Hertz Institute, Germany); Mladen Botsov (BMW Group, Germany)

1

Autonomous driving will rely on a multitude of connected applications with stringent quality of service (QoS) requirements in terms of low latency and high reliability. At the same time, passengers relieved of steering duty have the opportunity to enjoy infotainment services that are often associated with high data rates, e.g. video streaming. The simultaneous usage of such safety-related and infotainment services leads to diverse QoS requirements which are difficult to satisfy in current wireless networks. In an effort to address this issue, we propose a two-layer predictive resource allocation framework that leverages the services' properties and incomplete channel information. First, we optimize packet transmission times by a so-called statistical look-ahead scheduling to enhance the network's QoS and spectral efficiency based upon channel distribution information. Second, packets are forwarded to an online scheduler according to the outcome of this first optimization. Physical resources are assigned considering the services' QoS requirements and current channel state. We present a novel heuristic that performs real-time resource assignment. Simulations show that our approach has a potential for improving transmission reliability and spectral efficiency.

A Coalition Game for Backscatter-Aided Passive Relay Communications in Wireless-Powered D2D Networks

Xiaozheng Gao (Beijing Institute of Technology, China); Dusit Niyato (Nanyang Technological University, Singapore); Kai Yang and Jianping An (Beijing Institute of Technology, China)

0

With the rapid development of the backscatter technology, backscatter-aided passive relays can be employed to improve the performance of wireless-powered device-to-device (D2D) networks. In this paper, we investigate the strategy of how the D2D pairs cooperate with each other in the network, and formulate the problem as a coalition game. In the coalition game, the players are the D2D pairs, the payoff of a D2D pair is the amount of its transmitted data, and the action is that each D2D pair can choose to stay or switch its coalition. We then develop the preference relation and the switch rule, and further design the switch algorithm for the D2D pairs to form coalitions. Moreover, we analyse the stability of the coalition game and the complexity of the proposed strategy. Simulation results demonstrate that compared with the non-cooperative strategy, our developed cooperative strategy can efficiently improve the amount of the transmitted data.

Proportional Fairness through Dual Connectivity in Heterogeneous Networks

Pradnya Kiri Taksande and Prasanna Chaporkar (IIT Bombay, India); Pranav Jha (Indian Institute of Technology Bombay, India); Abhay Karandikar (IIT Bombay, India)

0

Proportional Fair (PF) is a scheduling technique to maintain a balance between maximizing throughput and ensuring fairness to users. Dual Connectivity (DC) technique was introduced by the 3rd Generation Partnership Project (3GPP) to improve the mobility robustness and system capacity in heterogeneous networks. In this paper, we demonstrate the utility of DC in improving proportional fairness in the system. We propose a low complexity centralized PF scheduling scheme for DC and show that it outperforms the standard PF scheduling scheme. Since the problem of dual association of users for maximizing proportional fairness in the system is NP-hard, we propose three heuristic user association schemes for DC. We demonstrate that DC, along with the proposed PF scheme, gives remarkable gains on PF utility over single connectivity and performs almost close to the optimal PF scheme in heterogeneous networks.

On Optimizing Signaling Efficiency of Retransmissions for Voice LTE

Chia-An Hsu and Kate Ching-Ju Lin (National Chiao Tung University, Taiwan); Yi Ren (University of East Anglia, United Kingdom (Great Britain)); Yu-Chee Tseng (National Chiao-Tung University, Taiwan)

0

The emergence of voice over LTE enables voice traffic transmissions over 4G packet-switched networks. Since voice traffic is characterized by its small payload and frequent transmissions, the corresponding control channel overhead would be high. Semi-persistent scheduling (SPS) is hence proposed in LTE-A to reduce such overhead. However, as wireless channels typically fluctuate, tremendous retransmissions due to poor channel conditions, which are still scheduled dynamically, would lead to a large overhead. To reduce the control message overhead caused by SPS retransmissions, we propose a new SPS retransmission protocol. Different from traditional SPS, which removes the downlink control indicators (DCI) directly, we compress some key fields of all retransmissions' DCIs in the same subframe as a fixed-length hint. Thus, the base station does not need to send this information to different users individually but just announces the hint as a broadcast message. In this way, we reduce the signaling overhead and at the same time, preserve the flexibility of dynamic scheduling. Our simulation results show that, by enabling DCI compression, our design improves signaling efficiency by 2.16×, and the spectral utilization can be increased by up to 60%.

Joint Optimization of Control and Resource Management for Wireless Sensor and Actuator Networks

Zhuwei Wang, Yuehui Guo, Yang Sun, Chao Fang and Wenjun Wu (Beijing University of Technology, China)

0

Wireless sensor actuator network (WSAN) emerges as a potential technology with the capacities of self-organizing communication and feedback control. In this paper, we present a novel collaborative optimization algorithm of plant control and system cost toward WSANs taking time delay into account. First, the WSAN model is formulated as a linear system with multi-path network structure. In order to provide effective control and reduce the usage of system resource, the quadratic cost function is introduced as the collaborative optimization problem in discrete-time domain. Then, a two-phase design is proposed to derive the design of optimal control for each given path in terms of a backward recursion. In addition, the best transmission path selection is obtained depending on minimal system power consumption. Finally, numerical simulations are utilized to show the effectiveness of the proposed algorithm in both traditional control system and load frequency control in the power grid application.

Physical Layer Security in Multi-Tag Ambient Backscatter Communications - Jamming vs. Cooperation

Ji Yoon Han, Mi Ji Kim, Junsu Kim and Su Min Kim (Korea Polytechnic University, Korea (South))

0

In this paper, we study on two different strategies - jamming and cooperation - for improving physical layer security in multi-tag ambient backscatter communications, which utilize existing radio frequency source. First of all, we propose a jamming-based multi-tag scheduling method, which selects a tag for data transmission and another tag(s) for artificial noise generation, in order to give a harmful impact to an eavesdropper. Next, we propose a cooperation-based multi-tag scheduling method, which selects a tag for data transmission and another tag(s) for cooperative transmission (the same data) in order to be helpful to a receiver. Through extensive simulations, we evaluate both strategies in terms of bit error rate and secrecy rate. Finally, we provide a guideline which strategy is better to apply for given environments depending on the total number of tags, channel gains between tags and the receiver, and the number of scheduled tags for jamming or cooperation.

Session Chair

Jianwei Huang (The Chinese University of Hong Kong, Hong Kong)

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Session T3-S7

Learning 2

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

End-Edge Coordinated Inference for Real-Time BYOD Malware Detection using Deep Learning

Xinrui Tan (State Key Laboratory of Information Security & Institute of Information Engineering, China); Hongjia Li, Liming Wang and Zhen Xu (Chinese Academy of Sciences, China)

0

Bring-Your-Own-Device (BYOD) has been widely viewed as a definite trend among enterprises in which employees bring and use their personal smartphones for work. Despite the perceived opportunities of increasing productivity and reducing costs, BYOD raises severe security and privacy concerns: the corporate networks and data are directly exposed to malware apps running on the personal smartphones. This highlights the necessity for performing real-time mobile malware detection in BYOD environments. Deep learning seems to be a natural choice to handle such detection, due to its state-of-the-art detection effectiveness. However, deep learning inference is usually too computationally complex for resource-constrained smartphones, and the communication overhead of cloud-based inference may be unacceptable. As a result, it is hard to seek the tradeoff between the real-time demand and optimality of detection accuracy. In this paper, we tackle this issue by proposing an end- edge coordinated inference approach that can support highly- accurate and average latency guaranteed malware detection. Our proposed approach integrates the early-exit and model partitioning methods to allow fast, correct and smartphone- localized inference to occur frequently. Extensive evaluations are carried out, demonstrating that our proposed approach offers a good compromise between detection accuracy and efficiency.

MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration

Ruoyun Chen and Hancheng Lu (University of Science and Technology of China, China); Yujiao Lu (USTC, China); Jinxue Liu (University of Science and Technology of China, China)

0

Under dynamic traffic, service function chain (SFC) migration is considered as an effective way to improve resource utilization. However, the lack of future network information leads to non-optimal solutions, which motivates us to study reinforcement learning based SFC migration from a long-term perspective. In this paper, we formulate the SFC migration problem as a minimization problem with the objective of total network operation cost under constraints of users' quality of service. We firstly design a deep Q-network based algorithm to solve single SFC migration problem, which can adjust migration strategy online without knowing future information. Further, a novel multi-agent cooperative framework, called MSDF, is proposed to address the challenge of considering multiple SFC migration on the basis of single SFC migration. MSDF reduces the complexity thus accelerates the convergence speed, especially in large scale networks. Experimental results demonstrate that MSDF outperforms typical heuristic algorithms under various scenarios.

L-FGADMM: Layer-Wise Federated Group ADMM for Communication Efficient Decentralized Deep Learning

Anis Elgabli (University of Oulu, Finland); Jihong Park (Deakin University, Australia); Sabbir Ahmed (University of Oulu, Finland); Mehdi Bennis (Centre of Wireless Communications, University of Oulu, Finland)

0

This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worker in L-FGADMM periodically communicates with two neighbors, in which the periods are separately adjusted for different layers of its deep neural network. A constrained optimization problem for this setting is formulated and solved using the stochastic version of GADMM proposed in our prior work. Numerical evaluations show that by less frequently exchanging the largest layer, L-FGADMM can significantly reduce the communication cost, without compromising the convergence speed. Surprisingly, despite less exchanged information and decentralized operations, intermittently skipping the largest layer consensus in L-FGADMM creates a regularizing effect, thereby achieving the test accuracy as high as federated learning (FL), a baseline method with the entire layer consensus by the aid of a central entity.

Root Cause Analysis of Noisy Neighbors in a Virtualized Infrastructure

Hedi Bouattour (ORANGE, France); Yosra Ben Slimen (Orange Labs Belfort, France); Marouen Mechtri and Hanane Biallach (Orange Labs Networks, France)

0

This paper proposes a model to identify the noise source in a virtualized infrastructure. This phenomenon appears when network functions running under virtual machines that are deployed on the same physical server compete for physical resources. First, an anomaly detection model is proposed to identify the machines that are in an abnormal state in the infrastructure by performing an unsupervised learning. An investigation of the root cause is later achieved by searching how anomalies are propagated in the system. To do this, a supervised learning of the anomaly propagation paths is proposed. A propagation graph is automatically created with a score assigned to its components. With a testbed created using Openstack, an experimentation study with real data is held giving promising results.

Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation

Madhusanka Dinesh Weeraratne Manimel Wadu and Sumudu Samarakoon (University of Oulu, Finland); Mehdi Bennis (Centre of Wireless Communications, University of Oulu, Finland)

1

In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to a centralized training-based solution, under imperfect channel state information (CSI). First, the problem is cast as a stochastic optimization problem over a predefined training duration and solved using the Lyapunov optimization framework. In order to learn and track the wireless channel, a Gaussian process regression (GPR)-based channel prediction method is leveraged and incorporated into the scheduling decision. The proposed scheduling policies are evaluated via numerical simulations, under both perfect and imperfect CSI. Results show that the proposed method reduces the loss of accuracy up to 25.8% compared to state-of-the-art client scheduling and RB allocation methods.

Session Chair

Anis Elgabli (University of Oulu, Finland)

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Session T3-S8

Sensor Network and IoT 2

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

On the Age of Information for Multicast Transmission with Hard Deadlines in IoT Systems

Jie Li and Yong Zhou (ShanghaiTech University, China); He Chen (The Chinese University of Hong Kong, Hong Kong)

0

We consider the multicast transmission of a real-time Internet of Things (IoT) system, where a server transmits time-stamped status updates to multiple IoT devices. We apply a recently proposed metric, named age of information (AoI), to capture the timeliness of the information delivery. The AoI is defined as the time elapsed since the generation of the most recently received status update. Different from the existing studies that considered either multicast transmission without hard deadlines or unicast transmission with hard deadlines, we enforce a hard deadline for the service time of multicast transmission. This is important for many emerging multicast IoT applications, where the outdated status updates are useless for IoT devices. Specifically, the transmission of a status update is terminated when either the hard deadline expires or a sufficient number of IoT devices successfully receive the status update. We first calculate the distributions of the service time for all possible reception outcomes at IoT devices, and then derive a closed-form expression of the average AoI. Simulations validate the performance analysis, which reveals that: 1) the multicast transmission with hard deadlines achieves a lower average AoI than that without hard deadlines; and 2) there exists an optimal value of the hard deadline that minimizes the average AoI.

Exact algorithms for barrier coverage with line-based deployed rotatable directional sensors

Zijing Ma, Shuangjuan Li and Dong Huang (South China Agricultural University, China)

0

Barrier coverage is an important coverage model for intrusion detection, which requires a chain of sensors across the deployment area with the adjacent sensors' sensing areas overlapping. Directional sensors are often dispersed from an airplane following a predetermined line. However, barrier coverage cannot be guaranteed after initial sensor deployment due to the sensors' random offsets and random orientations. Fortunately, directional sensors can rotate to mend the barrier gaps using this line-based sensor deployment model. Existing work proposed a greedy heuristic approach to mend the gaps by rotating sensors, but it cannot answer whether there exists a barrier. We fill in this gap by presenting an exact algorithm which can determine whether there exists a barrier. We first introduce the notion of feasible orientation range and then try to calculate each sensor's feasible orientation starting from the leftmost sensor. We also propose a fast algorithm of choosing the sensors' orientations from their feasible orientation ranges to form a barrier if there exists a barrier, or form a set of sub-barriers if there does not exist a barrier. Simulation results show that our algorithm outperforms the distributed algorithm in the existing work.

Genetic Algorithm-based Periodic Charging Scheme for Energy Depletion Avoidance in WRSNs

Huong Thi Tran (Ha Noi University of Science, Vietnam); Phi Le Nguyen (Hanoi University of Science and Technology, Vietnam); Huynh Thi Thanh Binh (HUST, Vietnam); Kien Nguyen (Chiba University, Japan); Minh Hai Ngo (Hanoi University of Science and Technology, Vietnam); Vinh Le (Vietnam National University, Vietnam)

0

Thanks to the advancements in wireless power transfer technologies, a new paradigm of wireless sensor network (WSNs) called wireless rechargeable sensor networks (WRSNs) has recently emerged. For a WRSN, designing an efficient charging schedule is a challenging issue due to the inherent constraints of WSNs. Although there have been many efforts to optimize the charging schedule, the existing works suffer from several critical problems. Firstly, they rarely tackle the dead node minimization problem, which is the ultimate objective of wireless charging. Secondly, most of the existing works assume impractical conditions, which include the unlimited battery capacity of the charger, and a fully charging scheme at the sensors. In this paper, aiming at minimizing the number of dead nodes, we propose a novel charging scheme based on the genetic algorithm. Our scheme works when the mobile charger has only limited capacity, and the sensors are charged partially at each charging round. The experiment results show that our proposed algorithm reduces the number of dead nodes significantly compared to other existing studies.

Simplified Theoretical Model based Self-adaptive Packet Reception Rate Estimation in Sensor Networks

Wei Liu, Yu Xia and Jian Xie (Chongqing University of Technology, China); Ming Xu (Nanjing University of Aeronautics and Astronautics, China); Rong Luo (Tsinghua University, China); Shunren Hu (Chongqing University of Technology, China); Xiaoyu Dang and Daqing Huang (Nanjing University of Aeronautics and Astronautics, China)

0

Real-time and accurate packet reception rate estimation is crucial for wireless sensor networks. However, existing approaches usually rely on offline data collection and training, which limits their generality. Specifically, models fitted by the test data acquired under specific conditions cannot be used in all environments and for arbitrary packet sizes. In this paper, a simplification method for the theoretical bit error rate model of IEEE 802.15.4 2.4 GHz physical layer is proposed, which is about 18 to 38 times faster than the original one. Then, with the simplified model, a lightweight packet reception rate estimation approach is designed, which is self-adaptive to different environments and arbitrary packet sizes. With the proposed approach, offline data collection and training are no longer needed, which will reduce deployment cost effectively. Compared with state-of-the-art approaches, estimate error of the proposed one is reduced by 2.46%~74.97% in different environments, and by 2.46%~62.00% for different packet sizes.

Decision Triggered Data Transmission and Collection in Industrial Internet of Things

Jiguang He (University of Oulu, Finland); Long Kong (Interdisciplinary Centre for Security, Reliability and Trust (SnT) & University of Luxembourg, Luxembourg); Tero Frondelius (R&D and Engineering, Wärtsilä, Vaasa, Finland); Olli Silvén and Markku Juntti (University of Oulu, Finland)

0

We propose a decision triggered data transmission and collection (DTDTC) protocol for condition monitoring and anomaly detection in the industrial Internet of things (IIoT). In the IIoT, the collection, processing, encoding, and transmission of the sensor readings are usually not for the reconstruction of the original data but for decision making at the fusion center. By moving the decision making process to the local end devices, the amount of data transmission can be significantly reduced, especially when normal signals with positive decisions dominate in the whole life cycle and the fusion center is only interested in collecting the abnormal data. The proposed concept combines compressive sensing, machine learning, data transmission, and joint decision making. The sensor readings are encoded and transmitted to the fusion center only when abnormal signals with negative decisions are detected. All the abnormal signals from the end devices are gathered at the fusion center for a joint decision with feedback messages forwarded to the local actuators. The advantage of such an approach lies in that it can significantly reduce the volume of data to be transmitted through wireless links. Moreover, the introduction of compressive sensing can further reduce the dimension of data tremendously. An exemplary case, i.e., diesel engine condition monitoring, is provided to validate the effectiveness and efficiency of the proposed scheme compared to the conventional ones.

Session Chair

Wei Liu (Chongqing University of Technology, P.R. China)

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Session T3-S9

Device-to-Device Communication

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Distributed Deep Learning Power Allocation for D2D network Based on Outdated Information

Jiaqi Shi, Qianqian Zhang, Ying-Chang Liang and Xiaojun Yuan (University of Electronic Science and Technology of China, China)

2

In the overlay D2D networks, multiple D2D pairs coexist with full frequency reuse resulting in complicated interference. Traditional centralized power control methods require instantaneous interference information and so are difficult to implement in a D2D network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core idea of the scheme is described as follows. First, we design linear filters with various sizes termed IFEs to extract the local interference patterns from the outdated interference information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, and so as to provide more effective power allocation strategies. Then, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network (RNN). The acquisition of the channel correlation can greatly improve the efficiency and the accuracy of our network according to our simulation results. It is worth noting that an input reduction process is also designed to reduce the space complexity from O(N2) to O(1) which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state- of-the-art power allocation algorithm.

Efficient Load Rearrangement of Small Cells with D2D Relay for Energy Saving and QoS Support

You-Chiun Wang (National Sun Yat-Sen University, Taiwan); Zong-Han Lin (National Sun Yat-sen University, Taiwan)

0

To satisfy the growing demand of wireless access, a mass of small cells are deployed in the service area to intensify signal quality and team up with macrocells. However, it spends lots of energy to keep the operation of small cells, which collides with the goal of green communications. In the paper, we propose an efficient load sharing (ELS) scheme to conquer this problem by transferring services of user equipments (UEs) among different cells. For each small cell in the off-peak period, its serving UEs will be adaptively taken over by other cells through handover or user-to-network relay (i.e., D2D relay). Thus, its base station can switch to the sleep mode and save energy. The above mechanism is also applied to the small cells whose base stations are overloaded for mitigating congestion. Simulation results show that ELS raises energy efficiency, curtails energy expense of the base stations in small cells, and provides better QoS support for UEs.

Intelligent Bluetooth Device to Device Connection Shift

Praneeth Juturu, Veerabhadrappa Chilakanti and Gowtham Babu (Samsung R&D Institute Bangalore, India)

0

Bluetooth Low Energy (BLE) is a wireless technology designed to provide reduced power consumption while maintaining similar communication range as compared to Classic Bluetooth [1]. Bluetooth (BT) and BLE are predominantly available in smartphones and are very useful for device-to-device communication. Most Bluetooth accessory (BTA) devices like BT headset, BT speaker and BT car-kit support only single connection. This behavior of BTA devices will hinder today's expectation of seamless continuity across multiple devices. User will encounter a connection failure, when user tries to connect a Smartphone to BTA device, which is already connected to other device. However, the reported reason for connection failure is "connection failed to be established" and is different from the exact reason, which is, "remote device is already connected". This is because BT device cannot differentiate between non-availability and the case where device is not responding. This will leave the user confused on which steps can ensure a successful connection. Device to device (D2D) communication can solve this connection issue and avoid confusion. To the best of our knowledge, this paper for the first time proposes a new concept that uses BLE's advertisement and scanning features to solve this connection issue by providing a connection shift from an already connected device, to the device that is trying to connect with the same BTA device. We have developed a prototype of our concept to present the results of connection shift and to provide analysis on difference in the time taken for connection shift during multiple attempts.

Contextual Multi-armed Bandit Based Pricing Scheme for Cooperative D2D Communications

Yue Meng and Zhaowei Wang (China Academy of Electronics and Information Technology (CAEIT), China)

0

Device-to-device (D2D) is a promising technology in 5G to improve network throughput, spectrum and energy efficiency, and transmission delay. It brings a new communication paradigm, cooperative D2D communication, which allows a user equipment (UE) to relay data for other UEs to the base station (BS). A relay UE consumes computing and communication resources, so that it should be compensated by setting price for the relay. In this paper, we investigate the pricing of relay UEs by modeling it with contextual multi-armed bandit (C-MAB). An online algorithm is designed to solve the C-MAB problem. Theoretical analysis shows that the algorithm converges to the optimal strategy asymptotically, and simulation results show that the C-MAB based pricing scheme results in both higher rewards for relay UEs and higher channel rate for the source UE.

D2D Assisted Beamforming for Coded Caching

Hamidreza Bakhshzad Mahmoodi (University of Oulu, Finland); Jarkko Kaleva (Solmu Technologies, Finland); Pooya Shariatpanahi (Institute for Research in Fundamental Sciences (IPM), Iran); Antti Tölli (University of Oulu, Finland)

0

Device-to-device (D2D) aided beamforming for coded caching is considered in a finite signal-to-noise ratio regime. A novel beamforming and resource allocation scheme is proposed where the local cache content exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and downlink transmission. In the D2D phase, users can autonomously share content with the adjacent users. The downlink phase utilizes multicast beamforming to simultaneously serve all users to fulfill the remaining content requests. A low complexity D2D-multicast mode selection algorithm is proposed with comparable performance to the optimal exhaustive search. We first explain the main procedure via one simple example and then present the general formulation. Furthermore, D2D transmission scenarios and conditions useful for minimizing the overall delivery time are identified. By exploiting the direct D2D exchange of file fragments, the common multicasting rate for delivering the remaining file fragments in the downlink phase is increased, providing greatly enhanced overall content delivery performance.

Session Chair

You-Chiun Wang (National Sun Yat-Sen University, Taiwan)

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Session T4-S4

URLLC

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Low-Complexity Centralized Multi-Cell Radio Resource Allocation for 5G URLLC

Ali Karimi (Aalborg University, Denmark); Klaus I. Pedersen (Nokia-Bell Labs, Aalborg University, Denmark); Preben Mogensen (Nokia–Bell Labs, Research Center Aalborg, Sweden)

0

This paper addresses the problem of downlink centralized multi-cell scheduling for ultra-reliable low-latency communications in a fifth generation New Radio (5G NR) system. We propose a low-complexity centralized packet scheduling algorithm to support quality of service requirements of URLLC services. Results from advanced 5G NR system-level simulations are presented to assess the performance of the proposed solution. It is shown that the centralized architecture significantly improves the URLLC latency. The proposed algorithm achieves gains of 99% and 90% URLLC latency reduction in comparison to distributed scheduling and spectral efficient dynamic point selection.

A Fully Coordinated New Radio-Unlicensed System for Ultra-Reliable Low-Latency Applications

Roberto Maldonado Cuevas (Aalborg University, Denmark); Claudio Rosa (Nokia, Denmark); Klaus Pedersen (Nokia - Bell Labs, Denmark)

0

Communications over the unlicensed spectrum are susceptible to be delayed by mandatory channel access mechanisms. Based on the need for improving the latency-reliability performance of New Radio-Unlicensed for supporting new use-cases, such as industrial applications, different types of channel access are evaluated in this paper. By using asynchronous and demand-driven channel access, it is shown that approximately 50% of the delay experienced by a downlink packet is due to listen before talk (LBT). Furthermore, UEs might be blocked by an unsuccessful uplink LBT losing the opportunity to transmit their previously scheduled data. As an alternative, synchronous channel access is evaluated. By using a coordinated LBT among the nodes, the channel access delay is reduced to a constant value. However, UEs can still be blocked when initiating their uplink transmissions due to neighbours transmissions. Motivated by this fact, an approach in which a central node is in charge of the frame selection is proposed. Therefore, all the nodes in the system are coordinated in both the channel access and the frame configuration. In this case, a latency reduction of 70% as compared to the previously mentioned alternatives is achieved at high loads and 99.9999% reliability.

Analysis of High-Reliable and Low-Latency Communication Enablers for New Radio Unlicensed

Roberto Maldonado Cuevas (Aalborg University, Denmark); Claudio Rosa (Nokia, Denmark); Klaus Pedersen (Nokia - Bell Labs, Denmark)

0

In this paper, the performance impact of several high-reliable and low-latency communications technology enablers in the unlicensed spectrum for standalone operation is evaluated. Firstly, a comparison between MulteFire (MF) and New Radio-Unlicensed (NR-U) is established. It is shown that higher sub-carrier spacings and shorter processing times provide clear latency reduction benefits. Additionally, different transmission time intervals (TTI) durations are evaluated. Shortening the TTI duration decreases the latency by a factor of 5.75 at 99.99% reliability and reduces the uplink Listen Before Talk (LBT) blocking probability by 18%. The possibility of having multiple switching points during the frame is also evaluated. It is concluded that having multiple switching points provides a latency reduction mainly due to the reduction of the number of channel accesses and the reduced gaps within the frame. A time-diversity technique to cope with high uplink LBT blocking probability is also evaluated. By combining the aforementioned features, a latency reduction factor of 31 is achieved when optimized NR-U and MulteFire performances are compared.

Robust URLLC Packet Scheduling of OFDM Systems

Jing Cheng and Chao Shen (Beijing Jiaotong University, China); Shuqiang Xia (ZTE Corporation, China)

0

In this paper, we consider the power minimization problem of joint physical resource block (PRB) assignment and transmit power allocation under specified delay and reliability requirements for ultra-reliable and low-latency communication (URLLC) in downlink cellular orthogonal frequency-division multiple-access (OFDMA) system. To be more practical, only the imperfect channel state information (CSI) is assumed to be available at the base station (BS). The formulated problem is a combinatorial and mixed-integer nonconvex problem and is difficult to tackle. Through techniques of slack variables introduction, the first-order Taylor approximation and reweighted-norm, we approximate it by a convex problem and the successive convex approximation (SCA) based iterative algorithm is proposed to yield sub-optimal solutions. Numerical results provide some insights into the impact of channel estimation error, user number, the allowable maximum delay and packet error probability on the required system sum power.

Efficient and Reliable Wireless Communications Through Multi-Connectivity and Rateless Coding

Philipp Schulz (Technische Universität Dresden, Germany); Andre N Barreto (Barkhausen Institut gGmbH, Germany & Universidade de Brasilia, Brazil); Gerhard Fettweis (Barkhausen Institut, Germany)

2

Achieving extremely high reliability is one of the key targets in the development of fifth generation mobile networks. To meet this ambitious aim, usually redundancy is introduced by simultaneously utilizing multiple links that are separated in frequency and/or space. However, current standards simply duplicate packets on these links, resulting in an inefficient high usage of resources that could have been used for other applications. To address this problem, rateless coding using multiple links is proposed for ultra-reliable communications, mathematically modeled, and evaluated in this paper. The benefits comprise efficient resource usage and a simplified feedback mechanism. Finally, they can be implemented in a technology- agnostic manner on application layer to easily exploit interface diversity.

Transceiver Design for Full-Duplex Ultra-Reliable Low-Latency Communications with Finite Blocklength

Keshav Singh (University College Dublin (UCD), Dublin, Ireland); Sudip Biswas (Indian Institute of Information Technology, Guwahati, India); Meng-Lin Ku (National Central University, Taiwan); Mark Flanagan (University College Dublin, Ireland)

0

In this paper, we jointly optimize the transceiver design and decoding error probability of a full-duplex (FD) ultra- reliable low-latency communication (URLLC) system, where the base-station (BS) operates in an FD mode while the uplink (UL) and downlink (DL) users work in a half-duplex (HD) mode. Accordingly, an optimization problem is formulated for an FD URLLC system under the finite blocklength (FBL) to maximize the achievable total (UL plus DL) rate subject to the reliability (i.e., the decoding error probability) of each link and total transmission power constraints at the UL user and the BS. We convexify the formulated non-convex problem by analyzing the problem structure. Next, an efficient iterative algorithm is proposed to find the near-optimal power allocation for the UL user and the transceiver weights for the BS. Simulation examples show the impact of the blocklength and decoding error probability on the system performance.

Session Chair

Hu Jin (Hanyang University, Korea)

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Session T4-S5

Vehicular Networks

Conference

4:00 PM — 5:30 PM KST

Local

May 26 Tue, 3:00 AM — 4:30 AM EDT

Energy-Efficient Power Control and Resource Allocation for V2V Communication

Lei Gao (Beijing University Of Posts And Telecommunications, China); Yanzhao Hou, Xiaofeng Tao and Min Zhu (Beijing University of Posts and Telecommunications, China)

0

In recent years, vehicle-to-vehicle (V2V) communication underlaying cellular networks has attracted more and more attention both in academy and industry. In this paper, guaranteeing the latency and reliability of vehicle users (VUEs), we study the resource management problem to maximize the energy efficiency of VUEs while considering the QoS requirement of cellular users (CUEs). Based on the Lyapunov optimization theory, a novel two-layer power control and resource allocation scheme is proposed by exploiting the properties of fractional programming. Simulation results show that the proposed scheme can achieves remarkable improvements in terms of EE and ensure the latency and reliability requirements of V2V communication.

Maximization of Con-current Links in V2V Communications Based on Belief Propagation

Xunchao Wu, Yanzhao Hou and Xiaofeng Tao (Beijing University of Posts and Telecommunications, China); Xiaosheng Tang (BeiJing University of Posts & Telecommunications, China)

0

In this paper, spectrum resource allocation in vehicle-to-vehicle (V2V) communications network is studied. Multiple V2V links can share one resource block (RB) for maximizing con-current links with the satisfaction of quality of service (QoS). The resource allocation problem is transformed into an inferential problem on a factor graph model. To solve this problem, the Belief Propagation based on Real-time Update of Messages (BPRUM) algorithm is proposed. In this method, the information matrix is updated after each message passing procedure is completed rather than after once iteration. From the simulation results, the proposed method achieves a significant increment in spectrum utilization compared to the existing algorithms.

Vehicle platooning schemes considering V2V communications: A joint communication/control approach

Tiago Rocha Goncalves (CentraleSupelec, France); Vineeth S Varma (CRAN & CNRS, France); Salah Eddine Elayoubi (CentraleSupélec, France)

0

This article addresses communication and control aspects of platooning systems with the related challenges introduced by the overlap of both areas. The main objective is to provide a dynamic control mechanism where the parameters of the well-known Predicted Cooperative Adaptive Cruise Control (PCACC) are adapted based on the observed quality of the V2V (Vehicle-to-Vehicle) communication links. Different from the state of the art, our main design goal is the minimization of inter-vehicular distances while being robust in terms of an extremely low probability of emergency braking. A new adaptive control scheme based on the offline optimization of the control gains is proposed. We evaluate the new approach in a highway scenario and show the improvements obtained by the dynamic adaptation of the control parameters over static control strategies.

Analytical Performance Evaluation of the Collective Perception Service in IEEE 802.11p Networks

Florian Alexander Schiegg (Leibniz University of Hanover & Robert Bosch GmbH, Germany); Daniel Bischoff (Technical University Darmstadt & Opel Automobile GmbH, Germany); Johannes Ruben Krost (Robert Bosch Car Multimedia GmbH, Germany); Ignacio Llatser (Robert Bosch GmbH, Germany)

1

A vast number of advanced driver assistance systems are currently being developed, from platoon controllers to intersection assistants. All these systems depend on the vehicle's capability to accurately sense its environment. In order to overcome line of sight and other constraints of standard on-board vehicle sensors, Vehicle-to-Everything (V2X) communication has been introduced. Through concepts like collaborative positioning, cooperative awareness and collective perception, traffic participants are enabled to share data gathered by their sensors, permitting to significantly enhance their environmental perception. This paper is aimed at supporting the current standardization efforts at the European Telecommunications Standards Institute (ETSI) with respect to the latter. It analyzes the performance of the collective perception service in IEEE 802.11p networks in terms of its impact on the traffic participant's gained environmental perception and draws conclusions for the further development of the service.

Path Optimization for Flying Base Stations in Multi-Cell Networks

Jongyul Lee and Vasilis Friderikos (King's College London, United Kingdom (Great Britain))

0

A crucial aspect when deploying Unmanned Aerial Vehicles (UAVs) to operate as flying base stations (FBSs) assisting 5G networks is path (trajectory) optimization. Even though this topic has received significant research attention for multiple UAVs located at the same base station (BS), the area of path optimization in the case of multiple UAVs located in different BSs serving user equipment (UE) or cluster points (CPs) in a multi cell environment received less attention. This paper addresses a mixed integer linear programming (MILP) formulation for FBS path optimization in terms of travel time considering a multi-cell environment which the BSs can act as the depots for multiple UAVs. Numerical investigations reveal that the proposed UAV path optimization approach can decrease the overall travel time for the deployment of the FBSs compared to other solutions that do explicitly optimize the case of multiple BSs and the UEs and/or CPs that belong within the coverage area of different BSs.

Session Chair

Yeongjin Kim (Inha University, Korea)

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Final Program

Program at a Glance

Main Conference

Opening Session on Tuesday

Sponsor Commercial/Welcome

Opening

Welcome message

TPC Report & Best paper award

Session Chair

Keynote

Next Steps and Challenges of 5G Network Evolution

Session Chair

Keynote

Challenges and Opportunities of 5G Mobile Edge Cloud

Session Chair

Millimeter-Wave Systems 1

An Analytical Model for Efficient Indoor THz Access Point Deployment

Opportunistic Hybrid Beamforming based on Adaptive Perturbation for mmWave Multi-User MIMO Systems

Maximum Sub-array Diversity for mmWave Network under RF Power Leakage and Distortion Noises

Enabling Massive Connections Using Hybrid Beamforming in Terahertz Micro-Scale Networks

Particle Swarm Optimization Inspired Low-complexity Beamforming for MmWave Massive MIMO Systems

Session Chair

Deep Learning for Wireless Communications 1

Adversarial Jamming Attacks on Deep Reinforcement Learning Based Dynamic Multichannel Access

Multi-Agent Deep Reinforcement Learning for Secure UAV Communications

Autoencoder based Friendly Jamming

Exploiting a low-cost CNN with skip connection for robust automatic modulation classification

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

Session Chair

Energy Efficiency in 5G and HetNets

Energy Harvesting-Enabled Full-Duplex DF Relay Systems with Improper Gaussian Signaling

Low-Complexity Hybrid Precoding and Combining Scheme Based on Array Response Vectors

Energy Efficiency Optimization for Beamspace Massive MIMO Systems with Low-Resolution ADCs

Energy-Efficient Design for Massive Access in B5G Cellular Internet of Things

Energy Efficient Ultra-Dense Network Using Long Short-Term Memory

Session Chair

Multiple Access

LiSCAN: Visible Light Uni-Directional Control Channel for Uplink Radio Access

A Joint Angle and Distance based User Pairing Strategy for Millimeter Wave NOMA Networks

An Adapted RFID Anti-collision Algorithm in a Dynamic Environment

A Simple Novel Idle Slot Prediction and Avoidance Scheme Using Prediction Bits for DFSA in RFID

ML Estimation and MAP Estimation for Device Activity in Grant-Free Massive Access with Interference

Session Chair

Learning 1

Robust Federated Learning Under Worst-case Model

SADroid: A Deep Classification Model for Android Malware Detection Based on Semantic Analysis

Semi-Federated Learning

Optimization-driven Hierarchical Deep Reinforcement Learning for Hybrid Relaying Communications

Auction based Incentive Design for Efficient Federated Learning in Cellular Wireless Networks

Session Chair

Sensor Network and IoT 1

Impact of Packet Routing Scheme on Post-Failure Industrial Wireless Sensor Networks

Wireless Backhaul Strategies for Real-Time High-Density Seismic Acquisition

Asymmetric Wake-up Scheduling based on Block Designs in Wireless Sensor Networks

On the Coverage Performance of Boolean-Poisson Cluster Models for Wireless Sensor Networks

FMUCR: Fuzzy-based Multi-hop Unequal Cluster Routing for WSN

Session Chair

Fog Computing and Networking

A Collaborative Task Offloading Scheme in D2D-Assisted Fog Computing Networks

An Energy-Efficient Mixed-Task Paradigm in Resource Allocation for Fog Computing

Latency Minimization with Optimum Workload Distribution and Power Control for Fog Computing

Mobility Prediction-Based Joint Task Assignment and Resource Allocation in Vehicular Fog Computing

Distributed V2V Computation Offloading Based on Dynamic Pricing Using Deep Reinforcement Learning

Session Chair

Streaming and Video

Distributed Video Analysis for Mobile Live Broadcasting Services

Optimal Buffering for High Quality Video Streaming in D2D Underlay Networks

Joint Quality Selection and Caching for SVC Video Services in Heterogeneous Networks

Reducing Latency in Interactive Live Video Chat Using Dynamic Reduction Factor

QFR: A QoE-driven Fine-grained Routing Scheme for Virtual Reality Video Streaming over SDN

Session Chair

Security and Privacy

Blockchain and Stackelberg Game Model for Roaming Fraud Prevention and Profit Maximization

A Secure Session Key Negotiation Scheme in WPA2-PSK Networks

Weighted Trustworthiness for ML Based Attacks Classification

Wearable Proxy Device-Assisted Authentication Request Filtering for Implantable Medical Devices

A New Privacy-Preserving Framework based on Edge-Fog-Cloud Continuum for Load Forecasting

Session Chair

NOMA (Non-Orthogonal Multiple Access)

Covert Non-Orthogonal Multiple Access