IEEE WCNC 2020

Session OPNWE

Opening Session on Wednesday

Conference

9:00 AM — 9:10 AM KST

Local

May 26 Tue, 7:00 PM — 7:10 PM CDT

WCNC 2021 Introduction

Hikmet Sari, Nanjing University of Posts and Telecommunications

4

This talk does not have an abstract.

Session Chair

Song Chong (KAIST, Korea (South))

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Session KNWE-S1

Keynote

Conference

9:10 AM — 9:50 AM KST

Local

May 26 Tue, 7:10 PM — 7:50 PM CDT

6G - A Step Beyond Stretching 5G

Prof. Gerhard Fettweis (Technische Universität Dresden)

18

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

Song Chong (KAIST, Korea (South))

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Session KNWE-S2

Keynote

Conference

9:50 AM — 10:30 AM KST

Local

May 26 Tue, 7:50 PM — 8:30 PM CDT

Into the Future Wireless

Dr. Jianmin Lu (Huawei)

12

The 5G has already been commercially deployed since last year and people, especially Korean customers, enjoy the benefit of 2C business. Although the standard of 3GPP R16 will be released shortly in 2020 and this will be the full formal 5G, i.e. IMT2020 compliant, the research and standard to evolve 5G will not stop. While 5G is opening the door of digital transformation of many aspects of our life, industry, business and even the whole society, the future of wireless is yet to be discovered. Since the first generation of mobile technology, the mobile industry has experienced significant growth driven by ‘subscription dividend’ and ‘traffic dividend’. The next dividend is believed to be the “connection dividend” or even “intelligence dividend”. In addition, sensing (including accurate positioning, imaging etc.) will be a novel capability of future network, enabling “everything sensing”, “everything connected” and “everything intelligent”. On the other hand, the sheer number of connected devices and objects will not only create unprecedented growth of data traffic and massive connections, but also create a substantial increase in energy consumption across all parts of the network. Energy efficiency in wireless networks is now a growing concern for network operators to not only reduce the network operation costs, but also as a social obligation, to reduce greenhouse gas emissions. Moreover, higher frequency (mmWave and THz) band provides abundant spectrum for Tbps data rate, while it also brings about paradigm shift in the whole system design. The research challenges and technology breakthroughs required to deliver the vision for future wireless will be presented in this talk.

Session Chair

Song Chong (KAIST, Korea (South))

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Session T1-S11

Millimeter-Wave Systems 2

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

Hybrid Multi-User Precoding for mmWave Massive MIMO in Frequency-Selective Channels

Shijian Gao (Colorado State University, USA); Xiang Cheng (Peking University, China); Liuqing Yang (Colorado State University, USA)

2

This paper investigates the transceiver design for downlink hybrid mmWave multi-user multi-carrier massive MIMO systems. In order to balance the processing complexity and the design flexibility, we adopt a prevalent hybrid precoding technique named hybrid block diagonalization (HBD) for downlink multi-user transmission. Aimed at maximizing the end-to- end mutual information (EEMI), a novel virtual EEMI assisted two-stage HBD scheme is judiciously devised. Apart from a low implementing complexity, the developed scheme is a more generic HBD solution, as it not only takes the frequency selectivity into account, but also removes the reliance on the high-resolution analog network. Simulations show that, even when applied with an inferior hardware configuration, the proposed HBD could still remarkably outperform its counterparts in terms of the EEMI performance at different levels of channel sparsity.

Secrecy Rate Analysis of mmWave MISO Ad Hoc Networks with Null Space Precoding

Ahmed F Darwesh and Abraham O Fapojuwo (University of Calgary, Canada)

0

Secure communication in the millimeter wave (mmWave) network is an important issue in the next-generation wireless network due to the massive improvement in the eavesdroppers' ability. This paper studies the secrecy rate performance of a mmWave multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. Firstly, to enhance the average achievable secrecy rate, an artificial noise (AN) transmission with null space linear precoder (Tx-AN/LP) is applied, taking into consideration the effect of blockage and Nakagami fading. Consequently, the tools of stochastic geometry are used to derive the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-AN/LP technique. Numerical and simulation results show that, using the Tx-AN/LP technique achieves more than three-fold improvement in the average secrecy rate over that without in the high power transmit regime (> 15 dBm). Moreover, the effect of increasing the colluding eavesdroppers' intensity without using the Tx-AN/LP technique is studied which provides a high deterioration in the average secrecy rate. Conversely, when the Tx-AN/LP technique is applied, increasing the colluding eavesdroppers' intensity has no negative impact on the secrecy rate. Furthermore, the proper power allocation between the message and AN signals which maximizes the average secrecy rate is computed. The results therefore show that the Tx-AN/LP technique is a useful technique to enhance the secrecy performance of mmWave MISO ad hoc network in the presence of colluding eavesdroppers.

SINR Coverage Analysis of Dense HetNets Over Fox's H-Fading Channels

Imene Trigui (University of Toronto, Canada); Sofiene Affes (INRS-EMT, Canada); Marco Di Renzo (Paris-Saclay University / CNRS, France); Dushantha Nalin K. Jayakody (National Research Tomsk Polytechnic University & Sri Lanka Technological Campus, Russia)

0

This paper embodies the Fox's H-transform theory into a unifying modeling and analysis of HetNets. The proposed framework has the potential, due to the Fox's H-functions versatility, of significantly simplifying the cumbersome analysis and representation of cellular coverage, while subsuming those previously derived for all known simple and composite fading models. The paper reveals important insights into the practice of densification in conjunction with signal-to-noise plus interference (SINR) thresholds and path-loss models.

Coverage Analysis of Relay Assisted Millimeter Wave Cellular Networks with Spatial Correlation

Simin Xu (Australian National University, Australia); Nan Yang (The Australian National University, Australia); Biao He (MediaTek USA Inc., USA); Hamid Jafarkhani (University of California, Irvine, USA)

0

We propose a novel analytical framework for evaluating the coverage performance of a millimeter wave (mmWave) cellular network where idle user equipments (UEs) act as relays. In this network, the base station (BS) adopts either the direct mode to transmit to the destination UE, or the relay mode if the direct mode fails, where the BS transmits to the relay UE and then the relay UE transmits to the destination UE. To address the drastic rotational movements of destination UEs in practice, we propose to adopt selection combining at destination UEs. New expression is derived for the signal-to-interference-plus- noise ratio (SINR) coverage probability of the network. Using numerical results, we first demonstrate the accuracy of our new expression. Then we show that ignoring spatial correlation, which has been commonly adopted in the literature, leads to severe overestimation of the SINR coverage probability. Furthermore, we show that introducing relays into a mmWave cellular network vastly improves the coverage performance. In addition, we show that the optimal BS density maximizing the SINR coverage probability can be determined by using our analysis.

Performance Analysis of Opportunistic Millimeter Wave Cloud-RAN with Nakagami-Blockage Channels

Behrouz Maham (Nazarbayev University, Kazakhstan)

1

In this paper, we consider an uplink Cloud Radio Access Network (RAN) transmission from a user served by multiple remote radio heads (RRHs) which are connected to each other via base band unit (BBU) pool which form a centralized processing. We assume opportunistic detection in which the best RRH is selected at a time to transfer the received message to BBU pool. In this case, we can achieve the diversity gain by choosing the proper RRH and reduce fronthaul traffic by occupying a single link. As another enabling technology for 5G and Beyond, we consider mmWave communications. Since LOS/NLOS components are essential in modeling mmWave bands, we use Nakagami-m channels. In addition, for blockage effect of mmWave bands, we assume random blockage model. For performance analysis, closed form expressions for outage probability and ergodic capacity are derived. Furthermore the analytical results are confirmed by comparing them with simulation results. It is shown that opportunistic scheme outperforms maximum ratio beamforming in which all RRHs are contributing in detection of the mobile user signal.

Session Chair

Jiho Song (University of Ulsan, Korea (South))

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Session T1-S12

Multi-Antenna System

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

Time Switching Protocol for Multi-Antenna SWIPT Systems

Seowoo Kang (Korea University, Korea (South)); Hoon Lee (Pukyong National University, Korea (South)); Sangwon Hwang and Inkyu Lee (Korea University, Korea (South))

0

In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) where a multi-antenna transmitter conveys information and energy simultaneously to a multi-antenna receiver equipped with time switching (TS) circuits for an energy harvesting (EH) mode and an information decoding (ID) mode. In contrast with conventional uniform TS (UTS) structure where all the receive antennas at the receiver apply a single TS circuit, to improve the SWIPT performance, we suggest a general dynamic TS (DTS) receiver architecture which consists of an individual TS circuit for each antenna. We aim to analyze the achievable rate-energy (R-E) tradeoff of the DTS system by jointly optimizing the covariance matrices at the transmitter and the time durations for the EH and the ID modes of the receive antennas. To determine the boundary points of the R-E region, we suggest the globally optimal algorithm for the rate maximization problem via convex optimization techniques. Numerical examples verify the efficacy of the proposed DTS over conventional UTS methods.

Adaptive Antenna Array with weight and antenna space control

Kenta Umebayashi and Yoshimasa Kimoto (Tokyo University of Agriculture and Technology, Japan); Antti Tölli (University of Oulu, Finland)

2

A typical adaptive antenna array based on weight control (AAA-W) with M antennas can suppress M — 1 interference signals. In this paper, we propose AAA based on not only weight but also antenna spacing control (AAA-WS) and investigate the basic performance of AAA-WS under the line-of- sight. At first, we show that AAA-WS with two antennas (M =3D 2) can sufficiently suppress more than two interference signals while the desired signal is enhanced. The inherent interference suppression capability of AAA-WS can be determined by the maximum antenna spacing and this fact is exhibited by analysis and Monte Carlo simulations. In addition, we will show that AAA-WS with two antennas can outperform AAA-W with more than two antennas. It is notable that the additional gain in AAA-WS compared to AAA-W can be around 18 dB.

Channel Correlation Cancelation-Based Hybrid Beamforming for Massive Multiuser MIMO Systems

Xinbo Wang, Li Guo, Chao Dong and Xidong Mu (Beijing University of Posts and Telecommunications, China)

3

In millimeter-wave (mmWave) communication systems, hybrid beamforming is regarded as an effective way to increase the spectral efficiency of the massive multiple-input multiple-output (MIMO) system. Assuming perfect channel state information (CSI) is known at the transmitter, we focus on a downlink massive multi-user MIMO system which supports multi-stream per user. In the above scenario, we investigate the hybrid beamforming problem with strong correlation between users’ channels, where the existing schemes have performance loss. To tackle this problem, this paper proposes the channel correlation cancelation-based hybrid beamforming (CCCHB) algorithm which considers the correlation between channels and decomposes the optimization of overall spectrum efficiency of the users to a series of sub-rate optimization problems. And the block diagonalization (BD) technique is used in the equivalent channel to eliminate inter-user interference. Simulation results illustrate that the performance of the proposed scheme outperforms the existing algorithm, especially significant when there exists high correlation between users’ channels.

IRS-Enhanced Wideband MU-MISO-OFDM Communication Systems

Hongyu Li, Rang Liu, Ming Li, Qian Liu and Xuanheng Li (Dalian University of Technology, China)

1

Intelligent reflecting surface (IRS) is considered as an enabling technology for future wireless communication systems since it can intelligently change the wireless environment to improve the communication performance. In this paper, an IRS-enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and the reflection of IRS to maximize the average sum-rate over all subcarriers. With the aid of the relationship between sum-rate maximization and mean square error (MSE) minimization, an efficient joint beamformer and IRS design algorithm is developed. Simulation results illustrate that the proposed algorithm can offer significant average sum-rate enhancement, which confirms the effectiveness of the use of the IRS for wideband wireless communication systems.

MF-based Dimension Reduction Signal Compression for Fronthaul-Constrained Distributed MIMO C-RAN

Fred Wiffen and Angela Doufexi (University of Bristol, United Kingdom (Great Britain)); Mohammud Z Bocus (Toshiba Research Europe Ltd, United Kingdom (Great Britain)); Woon Hau Chin (Toshiba Research Europe Limited, United Kingdom (Great Britain))

0

In this work we propose a fronthaul compression scheme for distributed MIMO systems with multi-antenna receivers, in which, prior to signal quantisation, dimension reduction is performed at each receiver by matched filtering the received signal with a subset of the local user channel vectors. By choosing these matched filter vectors based on global channel information, a high proportion of the potential capacity may be captured by a small number of signal components, which can then be compressed efficiently using local signal compression. We outline a greedy algorithm for selecting the matched filtering vectors for each receiver, and a local transform coding approach for quantising them, giving expressions for the resulting system sum and user capacities. We then show that the scheme is easily modified to account for imperfect CSI at the receivers. Numerical results show that with a low signal dimension the scheme is able to operate very close to the cut-set bound in the fronthaul-limited regime, and demonstrates significant improvements in rate-capacity trade-off versus local compression at all operating points, particularly at high SNR.

Session Chair

Daeyoung Park (Inha University, Korea (South))

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Session T1-S13

Information Theory and Capacity

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

How Does Channel Coding Affect the Design of Uplink SCMA Multidimensional Constellations?

Monirosharieh Vameghestahbanati, Ian D. Marsland, Ramy Gohary and Halim Yanikomeroglu (Carleton University, Canada); Javad Abdoli (Huawei Technologies Canada Co., Ltd., Canada)

0

Sparse code multiple access (SCMA) is a potential non-orthogonal multiple access candidate for future wireless systems. The key performance indicators (KPIs) of uplink SCMA multidimensional constellations (MdCs) that should be considered in their design process have recently been identified in conjunction with the LTE turbo code for different channel scenarios. However, it is questionable whether the same KPIs are applicable to designing MdCs when a different error correcting code is employed. In this paper, we investigate the effect of the high-rate and low-rate 5G low density parity check (LDPC) codes on determining KPIs in designing MdCs for uplink SCMA systems under various channel scenarios. Through simulations, we show that similar results to the LTE turbo coded case occur in the presence of 5G LDPC code, with one notable exception over one specific scenario. The exception is in the performance of one MdC, which has a low number of distinct points; its performance is significantly worse than predicted by the KPIs when the low- rate 5G LDPC code is employed. This phenomenon happens due to the inherent structure of the 5G LDPC code, in which we propose a pseudorandom interleaver to rectify the problem.

Capacity Analysis of Time-Indexed Media-based Modulation

Bharath Shamasundar (Indian Institute of Science, India); Lakshmi Narasimhan Theagarajan (Indian Institute of Technology, Palakkad, India); A. Chockalingam (Indian Institute of Science, India)

0

Time-indexed media-based modulation (TI-MBM) is an index modulation scheme where time slots in a transmission frame are indexed to convey additional information bits in media-based modulation (MBM). It was shown in the literature that, in frequency selective fading channels with inter-symbol interference, TI-MBM with cyclic-prefixed single-carrier (CPSC) scheme can achieve better transmission rates, bit error performance, and multipath diversity gain compared to conventional MBM. In this paper, we analyze the capacity of TI-MBM with CPSC, which has not been reported before. We show that, for a given channel matrix, selecting the index of the activated time slots and RF mirrors according to uniform distribution is suboptimal. We derive the probabilities with which time slots and RF mirrors in TI-MBM can be activated such that the achievable transmission rate is maximized. Further, we prove that the transmit symbols from a Gaussian mixture distribution, whose mixture weights are chosen to be the product of these probabilities, can achieve capacity.

Average Secrecy Capacity of SIMO κ-µ Shadowed Fading Channels with Multiple Eavesdroppers

Jiangfeng Sun and Hongxia Bie (Beijing University of Posts and Telecommunications, China); Xingwang Li (Henan Polytechnic University, China); Khaled M. Rabie and Rupak Kharel (Manchester Metropolitan University, United Kingdom (Great Britain))

0

In this paper, we analyze the security capability of single-input multiple-output wireless transmission systems over κ-µ shadowed fading channels in the presence of multiple eavesdroppers. Our security analysis relies on an important standard, i.e., average secrecy capacity which is more difficult and suitable for analyzing active eavesdropping scenario than secure outage probability and probability of strictly positive secrecy capacity. The novel expression of average secrecy capacity over κ-µ shadowed fading channels with multiple eavesdroppers is deduced. The results of Monte Carlo simulation fully prove the correctness of our theoretical derivation. Through the obtained results, we observe that large antenna quantity in the highest signal-to-noise ratio regime, small number of the eavesdroppers, and small signal-to-noise-ratio of eavesdropping link will enhance confidentiality of the system under consideration.

LDPC-Staircase Codes for Soft Decision Decoding

Viduranga Wijekoon, Emanuele Viterbo and Yi Hong (Monash University, Australia)

1

Staircase codes, a class of product-like codes, have been demonstrated to perform exceptionally well in optical transmission systems. Although they are predominantly used with BCH component codes and hard decision decoding, soft decision decoding has also been recently attempted, with BCH and polar code based staircase codes. We consider using LDPC codes as the component code of soft decoded staircase codes. Results demonstrate that these codes offer very good performance, with gains in the range of 0.5-1dB over soft decoded BCH-staircase codes, at a BER of 10-8. These can be further improved through the novel bit-flipping scheme we propose.

On the Construction of $G_N$-coset Codes for Parallel Decoding

Xianbin Wang, Huazi Zhang, Rong Li and Jiajie Tong (Huawei Technologies, Co. Ltd., China); Yiqun Ge (Huawei Technologies Canada Inc., Canada); Jun Wang (Huawei Technologies Co. Ltd, China)

0

In this work, we propose a type of GN-coset codes for parallel decoding. The parallel decoder exploits two equivalent decoding graphs of GN -coset codes. For each decoding graph, the inner code part is composed of independent component codes to be decoded in parallel. The extrinsic information of the code bits is obtained and iteratively exchanged between the two graphs until convergence. Accordingly, we explore a heuristic and flexible code construction method (information set selection) for various information lengths and coding rates. Compared to the previous successive cancellation algorithm, the parallel decoder avoids the serial outer code processing and enjoys a higher degree of parallelism. Furthermore, a flexible trade-off between performance and decoding latency can be achieved with three types of component decoders. Simulation results demonstrate that the proposed encoder-decoder framework achieves comparable error correction performance to polar codes with a much lower decoding latency.

Session Chair

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

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Session T2-S4

Resource Allocation

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

Effective Capacity based Resource Allocation for an Integrated Radar and Communications System

Yunfeng Liu, Zhiqing Wei and Zhiyong Feng (Beijing University of Posts and Telecommunications, China); Gordon Stüber (Georgia Institute of Technology, USA)

1

The integrated radar and communications system (IRCS) is promising for Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a tremendous challenge for the fusion center to collect detection information within the delay threshold. Based on only path loss of the channel, this paper performs power allocation to minimize the total transmit power while meeting the detection performance for radar and guaranteeing the latency violation probability (LVP) for communication. Using effective capacity theory, the latency constraint is expressed with introduced latency exponents. The resource allocation problem is non-convex and formulated to a convex one, which can be solved with a global optimum. Simulation results demonstrate the effectiveness of the proposed algorithm from the perspectives of the total transmit power and the latency of the communication links.

Traffic-Aware Beam Selection and Resource Allocation for 5G NR

Yu-Hsuan Liu and Kate Ching-Ju Lin (National Chiao Tung University, Taiwan)

2

3GPP has been defining 5G New Radio (NR), a new radio access technology, to enhance flexibility, scalability, and efficiency of 5G networks. An increasing data rate can be achieved by leveraging antenna arrays to adaptively form multiple directional beams and serve geo-distributed user equipments (UEs) concurrently. However, the imperfect beam pattern of an antenna array may create side lobes, leading to interuser interference. While most recent research focuses on beam selection that mitigates inter-user interference and maximizes the sum rate, we, however, notice that the selected beams may not be fully utilized. The root cause is that only a fixed set of beams can be configured at a time to serve a wide frequency band but some resource blocks (i.e., subcarriers) may not be able to be allocated to any UEs due to limited traffic demands. To address such inefficiency, this paper presents traffic-aware joint beam configuration and resource allocation, which explicitly considers UEs' traffic demands and configures beams that can be optimally utilized in all the RBs (i.e., the operational frequency band). Our simulation results show that our traffic-aware allocation configures beams with better utilization and achieve an effective throughput much higher than conventional maximal capacity configuration.

POET: An Energy-efficient Resource Management Mechanism for One-to-Many D2D Communications

Jun Huang (Chongqing University of Posts and Telecomm, China); Guohuan Wang (Chongqing Univ of Posts and Telecom & School of Commu. and Info. Eng., China); Cong-cong Xing (Nicholls State University, USA)

1

One-to-many Device-to-Device (D2D) communications, also refer to as D2D multicast communications, has been realized as an effective paradigm various practical settings. With the energy issue of mobile devices becomes more prominent, the energy efficiency of D2D multicast communication must be enhanced. In this paper, we propose POET, an energy-efficient resource management mechanism with joint Power cOntrol and channEl allocaTion for one-to-many D2D communications to address this issue. To be specific, by formulating the problem of energy efficiency maximization with QoS constraints on both cellular and D2D communications, we decompose this NP-hard problem into power control and channel allocation sub-problems. We present a gradient projection method for the first along with an iterative combinatorial auction algorithm for the second. Our preliminary results demonstrate that POET is lightweight and cost-effective in improving the energy efficiency of D2D multicast communications.

Cross-layer Resource Allocation in NOMA Systems with Dynamic Traffic Arrivals

Huiyi Ding (The University of Hong Kong, Hong Kong); Ka-Cheong Leung (Harbin Institute of Technology, Shenzhen, China)

2

Non-orthogonal multiple access (NOMA) has become a potential candidate to satisfy the heterogeneous demands in the fifth generation of wireless communication systems. With the optimization on the resource allocation, NOMA can further enhance the system performance. This paper proposes a cross-layer resource allocation framework for downlink NOMA systems. The problem is formulated as a stochastic problem to minimize the long-term total power consumption with dynamic traffic arrivals and time-varying channel under limited feedback. Then, this problem can be transformed to a rate control problem and a mixed-integer programming resource allocation problem solved at each time slot based on the Lyapunov optimization. To reduce the computational complexity, we devise an efficient suboptimal resource allocation algorithm with the dynamic penalty factor. The simulation results show that our proposed algorithms can reduce the power consumption compared with the two baseline algorithms while satisfying the QoS requirements.

Joint Power Allocation and Beam-forming Design for Dual-connectivity Wireless Networks

Minh Thang Nguyen, Jiho Song and Sungoh Kwon (University of Ulsan, Korea (South)); Kyung Sook Kim (ETRI, Korea (South))

3

In this paper, we propose a cooperating scheme to maximize network throughput while guaranteeing user quality of experience (QoE) demands in multiple-input-multiple-output (MIMO) systems. One of the aspired-to targets of the fifth generation (5G) network is to boost QoE everywhere, especially in the cell-edge areas. User equipments (UEs) in the edge areas are vulnerable to QoE violations, and they need dual connectivity from two nearby transmission points. Hence, UEs are categorized into two groups: single-connectivity and dual-connectivity. After classification, transmission power is allocated to maximize the network capacity while guaranteeing the minimum QoE. By comparing performance with a single connectivity-based algorithm and a fixed multi-connectivity-based algorithm, we show that our proposed algorithm not only satisfies all the UEs in the system but also maximizes the network capacity.

Session Chair

Bo Ji (Temple University, United States)

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Session T3-S10

Mobile Edge Computing 1

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems

Phuong-Duy Nguyen (INRS - University of Quebec, Canada); Long Bao Le (INRS, University of Quebec, Canada)

1

Network function Visualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network functions must be executed in a specific order. Moreover, the MEC technology enables computation offloading of service requests from mobile users to remote servers to potentially reduce energy consumption and processing delay for the mobile application. This paper considers the optimization of the computation offloading, resource allocation, and SFC placement in the multi-site MEC system. Our design objective is to minimize the weighted normalized energy consumption and computing cost subject to the maximum tolerable delay constraint. To solve the underlying mixed integer and non-linear optimization problem, we employ the decomposition approach where we iteratively optimize the computation offloading, SFC placement and computing resource allocation to obtain an efficient solution. Numerical results show the impacts of different parameters on the system performance and the superior performance of the proposed algorithm compared to benchmarking algorithms.

Task Offloading for End-Edge-Cloud Orchestrated Computing in Mobile Networks

Chuan Sun, Hui Li, Xiuhua Li, Wen Junhao and Qingyu Xiong (Chongqing University, China); Xiaofei Wang (Tianjin University, China); Victor C.M. Leung (University of British Columbia, Canada)

1

Recently, mobile edge computing has received widespread attention, which provides computing infrastructure via pushing cloud computing, network control, and storage to the network edges. To improve the resource utilization and Quality of Service, we investigate the issue of task offloading for End-Edge- Cloud orchestrated computing in mobile networks. Particularly, we jointly optimize the server selection and resource allocation to minimize the weighted sum of the average cost. A cost minimization problem is formulated under joint the constraints of cache resource and communication/computation resource of edge servers. The resultant problem is a Mixed-Integer Non-linear Programming, which is NP-hard. To tackle this problem, we decompose it into simpler subproblems for server selection and resource allocation, respectively. We propose a low-complexity hierarchical heuristic approach to achieve server selection, and a Cauchy-Schwards Inequality based closed-form approach to efficiently determine resource allocation. Finally, simulation results demonstrate the superior performance of the proposed scheme on reducing the weighted sum of the average cost in the network.

MeFILL: A Multi-edged Framework for Intelligent and Low Latency Mobile IoT Services

Ruichun Gu, Lei Yu and Junxing Zhang (Inner Mongolia University, China)

1

With the development of the cellular network in the last decade, the number of IoT devices is growing exponentially and IoT applications are becoming more complex with higher requirements for Key Performance Indicators (KPIs) such as latency, accuracy and energy consumption. To address these challenges, the edge computing paradigm is often adopted to push the computing capabilities to the edge servers nearest to end-users. However, the Quality of Experience (QoE) of IoT applications is still hard to guarantee because the nearest edge servers change while users roam around. In this paper, we propose MeFILL, a Multi-edged Framework for Intelligent and Low Latency mobile IoT applications, which reduces the latencies and improves the reliability with the seamless handover of IoT devices between edge servers and leverages the Distributed Deep Learning (DDL) collaboration among edge servers. The comparison experiments show that MeFILL can effectively optimize performance KPIs of mobile IoT applications.

MEC-Enabled Wireless VR Video Service: A Learning-Based Mixed Strategy for Energy-Latency Tradeoff

Chong Zheng (School of Information Science and Engineering, Southeast University, China); Shengheng Liu (Southeast University, P.R. China); Yongming Huang and Luxi Yang (Southeast University, China)

3

Mobile edge computing (MEC) has received broad attention as an effective network architecture and a key enabler of the wireless virtual reality (VR) video service which is expected to take a huge share of communication traffic. In this work, we investigate the scenario of multi-tiles-based wireless VR video service with the aid of MEC network, where the primary objective is to minimize the system energy consumption and the latency as well as to arrive at a tradeoff between these two metrics. To this end, we first cast the time-varying view popularity as a modelfree Markov chain and use a long short-term memory autoencoder network to predict its dynamics. Then, a mixed strategy, which jointly considers the dynamic caching replacement and the deterministic offloading, is designed to fully utilize the caching and computing resource in the system. The underlying multi- objective optimization problem is reformulated as a partially observable Markov decision process and solved by using a deep deterministic policy gradient algorithm. The effectiveness of the proposed scheme is confirmed by numerical simulations.

Learning Based Fluctuation-aware Computation offloading for Vehicular Edge Computing System

Zhitong Liu, Xuefei Zhang, Jian Zhang, Dian Tang and Xiaofeng Tao (Beijing University of Posts and Telecommunications, China)

1

Vehicular edge computing (VEC) is a promising paradigm to satisfy the ever-growing computing demands by offloading computation tasks to vehicles equipped with computing servers. One of the major challenges in VEC system is the highly dynamic and uncertain moving route of vehicular servers. In order to address this challenge, a particular kind of vehicles (i.e., buses) is adopted as moving servers with the pre-designated route and timetable. On this basis, a fluctuation-aware learning- based computation offloading (FALCO) algorithm based on multi-armed bandit (MAB) theory is proposed. Specifically, base stations (BSs) are regarded as agents to learn the state of moving server so as to construct a stable observation set in the dynamic vehicular environment. In addition, the softmax function is applied to indicate the probability for each decision, which provides more flexible policies for obtaining better results. Simulation results demonstrate that our proposed FALCO algorithm can improve delay performance compared with the other existing learning algorithms.

Session Chair

Shengheng Liu (Southeast University & Purple Mountain Laboratories, China)

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Session T3-S11

NOMA (Non-Orthogonal Multiple Access)

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

Online maneuver design for UAV-enabled NOMA systems via reinforcement learning

Yuwei Huang (University of Science and Technology of China, China); Xiaopeng Mo (GDUT, China); Jie Xu (The Chinese University of Hong Kong, Shenzhen, China); Ling Qiu (University of Science and Technology of China, China); Yong Zeng (Southeast University, China)

0

This paper considers an unmanned aerial vehicle (UAV)-enabled uplink non-orthogonal multiple-access (NOMA) system, where multiple users on the ground send independent messages to a UAV via NOMA transmission. We aim to design the UAV's dynamic maneuver in real time for maximizing the sumrate throughput of all ground users over a finite time horizon. Different from conventional offline designs considering static user locations under deterministic or stochastic channel models, we consider a more challenging scenario with mobile users and segmented channel models, where the UAV only causally knows the users' (moving) locations and channel state information (CSI). Under this setup, we first propose a new approach for UAV dynamic maneuver design based on reinforcement learning (RL) via Q-learning. Next, in order to further speed up the convergence and increase the throughput, we present an enhanced RL-based approach by additionally exploiting expert knowledge of well- established wireless channel models to initialize the Q-table values. Numerical results show that our proposed RL-based and enhanced RL-based approaches significantly improve the sumrate throughput, and the enhanced RL-based approach considerably speeds up the learning process owing to the proposed Q-table initialization.

NOMA based VR Video Transmission Exploiting User Behavioral Coherence

Ping Xiang, Hangguan Shan, Zhaoyang Zhang and Yu Lu (Zhejiang University, China); Tony Q. S. Quek (Singapore University of Technology and Design, Singapore)

0

In this work, we study the cooperative and noncooperative transmission schemes design for live VR video broadcast scenarios by utilizing non-orthogonal multiple access (NOMA), considering that users' viewports partly overlap due to behavioral coherence. To characterize the performance of the proposed cooperative and non-cooperative transmission schemes, the exact and asymptotic expressions of outage probability, as well as the average outage capacity under imperfect successive interference cancellation (SIC), are derived, respectively. Based on the asymptotic outage probability results, we optimize the power allocation to maximize the average outage capacity of the proposed schemes. Finally, simulation results demonstrate that both of the proposed schemes can achieve a considerable performance gain over the traditional orthogonal multiple access (OMA) scheme in average outage capacity, and each of the proposed schemes has its advantages and applicable scenarios.

Joint User Association and Resource Allocation for NOMA-Based MEC: A Matching-Coalition Approach

Guangyuan Zheng, Chen Xu and Liangrui Tang (North China Electric Power University, China)

0

Mobile edge computing (MEC) is regarded as a key technology to reduce the network pressure from the computingintensive and latency-sensitive applications in future wireless networks. Non-orthogonal multiple access (NOMA) can achieve high spectral efficiency by allowing multiple users to reuse the same resources. In this paper, we consider a novel NOMA-based MEC system to improve the energy efficiency during task offloading process. With multiple access points (APs) being deployed, the optimization problem is joint user association and resource allocation while the objective is to minimize the total energy consumption of all users subject to the task execution deadline. We formulate the problem as a many-to-one matching game with externality due to the co-channel interference among users, and then, propose a matching-coalition approach coupled with computing resource allocation and power control. Simulation results show that the proposed approach can efficiently reduce the total energy consumption in comparison to other simplified approaches.

User Scheduling and Energy Management with QoS Provisioning for NOMA-based M2M Communications

Chunhui Feng and Qinghai Yang (Xidian University, China); Meng Qin (School of Electronics and Computer Engineering, Peking University, China); Kyung Sup Kwak (Inha University, Korea (South))

0

Non-orthogonal multiple access (NOMA) is considered as a potential technique to relieve the congestion due to concurrent access from massive devices in machine-to-machine (M2M) communication system. However, the cochannel interference caused by NOMA, and the energy budget of machinetype devices (MTDs), become the bottleneck to further improve the system performance. Given above issues, we formulate the joint user scheduling and energy management problem as a stochastic optimization problem. Specifically, the goal of the problem is to maximize the long-term average sum rate under the constraint of all MTDs' quality-of-service (QoS) requirements. For tractability, the stochastic problem is firstly transformed into two static subproblems based on Lyapunov optimization. Then, using successive convex approximation (SCA) method, we design an effective algorithm to deal with the joint user scheduling and power allocation subproblems, which is a mixed integer and non-convex programming (MINCP). Simulation results demonstrate that our proposed algorithm has a good performance in convergence and outperforms other schemes in terms of user satisfaction.

Joint Reflection Coefficient Selection and Subcarrier Allocation for Backscatter Systems with NOMA

Farhad Dashti Ardakani (The University of British Columbia, Canada); Vincent W.S. Wong (University of British Columbia, Canada)

0

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies that enable low power communication for the Internet of Things (IoT) devices. In this paper, we consider a multicarrier NOMA (MC-NOMA) backscatter communication system. The objective is to maximize the aggregate data rate of the system by jointly optimizing the reflection coefficients and subcarrier allocation. The formulated problem is nonconvex and exhibits hidden monotonicity structure. To obtain the optimal solution, we propose an algorithm based on discrete monotonic optimization. The proposed algorithm can be considered as a performance benchmark. We also transform the nonconvex problem to another problem by using difference of convex functions and successive convex approximation and propose an algorithm to obtain a suboptimal solution in polynomial time. Simulation results show that the suboptimal scheme achieves an aggregate data rate close to the proposed optimal scheme. Results also show that our proposed schemes provide a higher aggregate data rate than the orthogonal multiple access (OMA) scheme.

Session Chair

Hangguan Shan (Zhejiang University, China)

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Session T3-S12

Vehicular Network 1

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

C2RC: Channel Congestion-based Re-transmission Control for 3GPP-based V2X Technologies

Gaurang Naik and Jung-Min (Jerry) Park (Virginia Tech, USA); Jonathan Ashdown (United States Air Force, USA)

2

The 3rd Generation Partnership Project (3GPP) is actively designing New Radio Vehicle-to-Everything (NR V2X)— a 5G NR-based technology for V2X communications. NR V2X, along with its predecessor Cellular V2X (C-V2X), is set to enable low-latency and high-reliability communications in high-speed and dense vehicular environments. A key reliability-enhancing mechanism that is available in C-V2X and is likely to be reused in NR V2X is packet re-transmissions. In this paper, using a systematic and extensive simulation study, we investigate the impact of this feature on the system performance of C-V2X. We show that statically configuring vehicles to always disable or enable packet re-transmissions either fails to extract the full potential of this feature or leads to performance degradation due to increased channel congestion. Motivated by this, we propose and evaluate Channel Congestion-based Re-transmission Control (C2RC), which, based on the observed channel congestion, allows vehicles to autonomously decide whether or not to use packet re-transmissions without any role of the cellular infrastructure. Using our proposed mechanism, C-V2X-capable vehicles can boost their performance in lightly-loaded environments, while not compromising on performance in denser conditions.

A User Association Policy for UAV-aided Time-varying Vehicular Networks with MEC

Bingqing Hang, Biling Zhang and Li Wang (Beijing University of Posts and Telecommunications, China); Jingjing Wang and Yong Ren (Tsinghua University, Beijing, China); Zhu Han (University of Houston, USA)

0

Multi-access edge computing (MEC) is viewed as a promising technology to improve the real time video service in vehicular networks. However, in the traditional vehicular networks, the road side units (RSUs) are usually only equipped with communication modules, and the unmanned aerial vehicles(UAVs) are seldom used. In this paper, a new UAV-aided time-varying vehicular network is introduced for vehicle users (VUEs) to obtain better experience, where the RSUs and the UAV are equipped with MEC servers for the real time video transcoding. Considering that the video service always lasts for a period of time, we investigate the user association policy from a long-term perspective. Specifically, to characterize the time- varying features of communication links and the heterogeneity of available resources, we theoretically derive the achievable video chunks and link reliability based on the vehicle mobility model and content caching model. Then, the user association problem is formulated as the utility optimization problem, where both the VUE's quality of experience (QoE) and handover cost are taken into consideration. Furthermore, we propose an improved Dijkstra algorithm to solve the original NP-hard problem after it is transformed to a shortest path selection problem. Finally, by numerical results, we verify that the proposed scheme out-performs existing schemes in terms of the VUE's QoE and the handover numbers.

Delay Sensitive Large-scale Parked Vehicular Computing via Software Defined Blockchain

Yuanyuan Cao and Yinglei Teng (Beijing University of Posts and Telecommunications, China); F. Richard Yu (Carleton University, Canada); Victor C.M. Leung (University of British Columbia, Canada); Ziqi Song and Mei Song (Beijing University of Posts and Telecommunications, China)

0

To utilize the potential commutating resources of parked vehicles (PVs) in the large parking lot, we design a large- scale parked vehicular computing system via software defined blockchain. However, the parking time for PVs is uncertain and some computational services have delay requirements. Therefore, in this paper, we propose a delay-sensitive joint blockchain parameters and resource optimization framework including block size and block generation time, as well as the offloading strategy and computing frequency adjustment. Such a design causes the problem to be highly coupled and non-convex, for which we use an alternating optimization (AO) strategy and perform multiple transformations to ensure convexity. Finally, the simulation results show the effectiveness of the proposed scheme.

Resource Allocation for AoI-Constrained V2V Communication in Finite Blocklength Regime

Shuai Gao and Meixia Tao (Shanghai Jiao Tong University, China)

0

The freshness of information is an important indicator for critical message exchange in vehicle-to-vehicle (V2V) communications. In this paper, we study a resource allocation problem to minimize the long-term power consumption under age of information (AoI) constraints in the finite blocklength (FBL) regime. Due to high reliability requirement, we consider the AoI violation probability which consists of decoding error probability and queue length violation probability. To ensure a short tail of the AoI distribution, we impose statistical constraints to the queue length utilizing extreme value theory (EVT). Applying Lyapunov optimization technique, the long-term problem is transformed into the drift-plus-penalty problem, which can be solved in each slot via a two-step method. In addition, in order to achieve the optimal power control and decoding error probability, we propose an efficient iterative algorithm and show the convexity of the optimization problem in each step. Simulation results show that our scheme achieves high reliability and short AoI tail compared to the baseline in the FBL regime.

A Spectrum Aware Mobility Pattern Based Routing Protocol for CR-VANETs

Sharmin Akter and Nafees Mansoor (University of Liberal Arts Bangladesh, Bangladesh)

2

Cognitive radio technology offers an important function in the efficient utilization of the radio spectrum. Besides, it is expected that CR-enabled vehicular ad-hoc networks (CR-VANETs) enrich the communication performance of the existing vehicular networks (VANETs). However, to ensure efficient performance in a multi-hop communication, the routing protocol in CR-VANETs needs to consider the autonomous mobility of the vehicles as well as the stochastic availability of the channels. Hence, this paper introduces a spectrum-aware mobility pattern based reactive routing protocol for CR-VANET. The proposed protocol accommodates the dynamic behavior and selects a stable transmission path from a source node to the destination. Therefore, the proposed protocol is outlined as a weighted graph problem where the weight for an edge is measured based on a parameter termed as NHDF (Next-hop Determination Factor). The NHDF implicitly considers mobility patterns of the nodes and channel availability to select the optimum path for communication. Therefore, in the proposed routing protocol, the mobility pattern of a node is defined from the viewpoint of distance, speed, direction, and node's reliability. Furthermore, the spectrum awareness in the proposed protocol is measured over the number of shared common channels and the channel quality. It is anticipated that the proposed protocol shows efficient routing performance by selecting stable and secured paths from source to destination. Simulation is carried out to assess the performance of the protocol where it is witnessed that the proposed routing protocol outperforms existing ones.

Session Chair

Anis Zarrad (University of Birmingham, UAE)

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Session T4-S6

Edge Computing and Caching

Conference

11:00 AM — 12:30 PM KST

Local

May 26 Tue, 9:00 PM — 10:30 PM CDT

Overlay Coded Multicast for Edge Caching in 5G-Satellite Integrated Networks

Xinmu Wang, Hewu Li, Tianming Lan and Qian Wu (Tsinghua University, China)

0

Edge caching in 5G networks shortens latency and alleviates the backhaul. Bringing contents from the core network to the caches is a critical issue. When the satellite is integrated with the terrestrial 5G system, an overlay can be formed for cost-efficient content delivery. Multicast delivery over satellite is a promising scheme due to the broadcast nature of the wireless medium and wide coverage. Requests for popular content at nearby times can be aggregated through a multicast stream for bandwidth efficiency. However, directly multicast to a large audience from the satellite suffers from the problems led by fading channels and the flat topologies. The overlay architecture provides new solutions to handle the drawbacks of satellite multicast, e.g., channel and reception diversity, the difficulty of loss recovery and feedback explosion. We explicitly introduce the overlay architecture based on the configuration of multiple multicast groups and the merging of base station clusters for each communication session. The operation of this overlay is further explained. Besides, we also apply network coding to the multicast and cache networks to improve data recovery and bandwidth efficiency. Both theoretical analysis and numerical experiments demonstrate the optimization of network performance.

An EPEC Analysis among Mobile Edge Caching, Content Delivery Network and Data Center

Yue Yu (Southeast University, China); Xiao Tang (Northwestern Polytechnical University, China); Yiyong Zha and Yunfei Zhang (Tencent, China); Tiecheng Song (National Mobile Communications Research Laboratory, Southeast University, China); Zhu Han (University of Houston, USA)

0

Mobile edge caching (MEC), content delivery network (CDN) and data center (DC) serve Internet content providers (ICPs) with different advantages and disadvantages. In this paper, we propose an equilibrium problem with equilibrium constraints (EPEC) to investigate the delivery strategies for files and pricing mechanisms for MEC, CDN, and DC. At the upper level, MEC and CDN predict the files' rational delivery strategies and set the delivery price for each byte to provide the content delivery service. DC serves as origin servers to provide free content delivery service. At the lower level, the files observe the price strategy and determine their delivery strategies. In the proposed EPEC problem, there exist Nash equilibriums, which are coupled with each other, at both the upper level and lower level. We adopt a block coordinate descent (BCD) method to find the equilibrium solutions at both the upper level and lower level. Simulation results show that our proposed approach yields high utilities at the equilibrium.

Design and Implementation on a LoRa System with Edge Computing

Zhiming Liu, Qihao Zhou and Lu Hou (Beijing University of Posts and Telecommunications, China); Rongtao Xu (Beijing Jiaotong University, China); Kan Zheng (Beijing University of Posts&Telecommunications, China)

0

The Long Range (LoRa) systems usually process all the computing tasks on the LoRa central server remotely, which brings large latency to Internet of Things (IoT) applications. In this paper, we propose a new design of a LoRa system with edge computing at the LoRa gateway. Our design enables that some of the time computing tasks for latency-sensitive applications can be dealt with timely. The implementation details of the LoRa gateway are presented along with functionality of each component. Finally, comprehensive experiments are conducted to evaluate the performance of the proposed system. The results show that the proposed system can decrease the latency of IoT applications and balance the workloads between the LoRa central server and the LoRa gateway.

Collaborative Edge Computing and Caching in Vehicular Networks

Zhuoxing Qin and Supeng Leng (University of Electronic Science and Technology of China, China); Jihua Zhou (Institute of Computing Technology, Chinese Academy of Sciences, China); Sun Mao (University of Electronic Science and Technology of China, China)

0

Mobile Edge Computing (MEC) can significantly promote the development of Internet of Vehicles (IoV) for providing a low-latency and high-reliability environment. Nevertheless, a huge amount of sensor data or computing requirements generated by massive vehicles in adjacent area may be duplicated. In order to realize the efficient diffusion of information, we propose a hierarchical end-edge framework with the aid of deep collaboration among data communication, computation offloading and content caching to minimize network overheads. Specially, duplicated perceived data and computation results are cached in advance to decrease repeated data uploading and duplicated computation in offloading process. In addition, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, and the deep deterministic policy gradient (DDPG)-based resource allocation scheme is utilized to obtain a sub-optimal solution with low computation complexity. Performance evaluation demonstrates that the proposed scheme can significantly reduce network overheads compared with other benchmark methods.

Latency Guaranteed Edge Inference via Dynamic Compression Ratio Selection

Xiufeng Huang and Sheng Zhou (Tsinghua University, China)

0

With the development of intelligent Internet of things (IoT) devices, implementing machine learning algorithms at the network edge has become essential to many applications, such as autonomous driving, environment monitoring. However, the limited computation capability and energy constraint results in difficulties of running complex machine learning algorithms on edge devices subject to latency requirements, and one solution is to offload the computation tasks to the edge server. However, the wireless transmission of raw data from devices to the server is time consuming and may violate the latency requirement. To this end, lossy data compression can be helpful, but the information loss may lead to erroneous learning result, e.g., wrong classification. In this paper, we propose a transmission scheme with compression ratio selection for inference tasks with task completion latency guarantee. By dynamically selecting the optimal compression ratio with the awareness of the remaining latency budget, more tasks can be timely completed and get the correct inference results under the communication resource constraint. Furthermore, retransmitting less compressed data of tasks with erroneous inference results can potentially enhance the average accuracy. However, it is often hard to know whether the inference result is correct or not. We therefore use uncertainty to estimate the confidence of the results, and based on that, jointly optimize the retransmission and compression ratio selection.

Session Chair

Youngbin Im (UNIST, Korea)

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Session DEMO-S1

WCNC 2020 Demo

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

MmWave Lens MIMO

Sang-Hyun Park and Dongsoo Jun (Yonsei University, Korea (South)); Byoungnam Kim (Sensor View, Korea (South)); Dong Ku Kim and Chan-Byoung Chae (Yonsei University, Korea (South))

6

This talk does not have an abstract.

Millimeter-Wave Massive MIMO Testbed with Hybrid Beamforming

MinKeun Chung, Liang Liu, Ove Edfors and Fredrik Tufvesson (Lund University, Sweden)

8

This talk does not have an abstract.

In-Vessel Molecular MIMO Communications

Changmin Lee, Bonhong Koo and Chan-Byoung Chae (Yonsei University, Korea (South))

6

This talk does not have an abstract.

Demonstration of Reconfigurable Metasurface for Wireless Communications

Nguyen Minh Tran, Amri Muhammad Miftahul, Dong Soo Kang, Je Hyeon Park and Mi Hyun Lee (Sungkyunkwan University, Korea (South)); Dong In Kim (Sungkyunkwan University (SKKU), Korea (South)); Kae Won Choi (Sungkyunkwan University, Korea (South))

2

This talk does not have an abstract.

A Unified Platform of Free-Space Optics for High-Quality Video Transmission

Hong-Bae Jeon and Hyung-Joo Moon (Yonsei University, Korea (South)); Soo-Min Kim (University of Yonsei, Korea (South)); Do-Hoon Kwon, Joon-Woo Lee, Sang-Kook Han and Chan-Byoung Chae (Yonsei University, Korea (South))

5

This talk does not have an abstract.

Wireless VR/Haptic Open Platform for Multimodal Teleoperation

Taehun Jung and Hanju Yoo (Yonsei University, Korea (South)); Youna Jin and Chae Eun Rhee (Inha University, Korea (South)); Chan-Byoung Chae (Yonsei University, Korea (South))

6

This talk does not have an abstract.

A Network Slicing Solution for Flexible Resource Allocation in SDN-based WLANs

Estefania Coronado (Fondazione Bruno Kessler, Italy); Blas Gómez (University of Castilla-La Mancha, Spain); Roberto Riggio (Fondazione Bruno Kessler, Italy)

2

This talk does not have an abstract.

Mobility Enhanced RPL for General Mobility Scenarios

Hongchan Kim, Jiseok Youn and Hyung-Sin Kim (Seoul National University, Korea (South)); Sung-Guk Yoon (Soongsil University, Korea (South)); Saewoong Bahk (Seoul National University, Korea (South))

2

This talk does not have an abstract.

A Reinforcement Learning based Flexible Duplex Systems for B5G with Sub-6 GHz

Soo-Min Kim (University of Yonsei, Korea (South)); Han Cha, Seong-Lyun Kim and Chan-Byoung Chae (Yonsei University, Korea (South))

5

This talk does not have an abstract.

Experimental Study of Capture Effect in Smartphones and Wi-Fi Access Points

Egor Onore Endovitskiy (Moscow Institute of Physics and Technology & Institute for Information Transmission Problems of Russian Academy of Sciences, Russia); Evgeny Khorov (IITP RAS, Russia); Aleksey Kureev (IITP RAS & MIPT, Russia); Ilya Levitsky (IITP & IITP RAS, Canada)

2

This talk does not have an abstract.

Latency Control for Interactive Five Degree-of-Freedom View Exploration Systems

Won-Ki Seo (Inha University, Korea (South)); Taehun Jung, Hanju Yoo and Chan-Byoung Chae (Yonsei University, Korea (South)); Chae Eun Rhee (Inha University, Korea (South))

2

This talk does not have an abstract.

Session Chair

Sang-Hyo Kim (Sungkyunkwan University, Korea)

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Session T1-S14

Signal Processing for Millimeter-Wave and THz Communications

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Measurement of 2x2 LoS Terahertz MIMO Channel

Suresh Singh, Thanh Le and Ha Tran (Portland State University, USA)

1

This paper examines the performance of a 2x2 Line of Sight (LoS) Multiple Input Multiple Output (MIMO) channel at three terahertz frequencies - 340 GHz, 410 Ghz, and 460 GHz. While theoretical models predict very high channel capacities, we observe lower capacity which is explained by asymmetric transmit-to-receive signal strengths as well as due to signal attenuation over longer distances. Overall, however, we note that at 460 Ghz, channel capacity of higher than 12 bps/hz is possible even at sub-optimal inter-antenna spacings (for different distances). An important observation is also that we need to maintain appropriate receive signal levels at receive antennas in order to improve capacity.

Precoding with the Assistance of Attitude Information in Millimeter Wave MIMO System

Shiyu Zhou (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)

1

Digital beamforming (DBF) is considered as an efficient method to overcome the high propagation loss of millimeter wave (mmWave) communication, but the acquisition of channel state information (CSI) brings huge training overhead, especially in high mobility scenarios. To tackle this challenge, we consider using attitude information from motion sensors to reduce the training overhead of DBF in this paper. We first analyze the characteristics of mmWave uplink channel when the attitude of user equipment (UE) rotates, and it shows that only the precoder needs to be redesigned after the rotation. Therefore, we develop a novel attitude information aided precoding algorithm, which approaches the performance of conventional singular value decomposition (SVD) algorithm. The proposed algorithm reduces the channel estimation and feedback overhead significantly compared to the conventional one. Finally, the simulation results show that the proposed algorithms allow mmWave systems to approach their performance limits.

A Novel 3D Space-Time-Frequency Non-Stationary Channel Model for 6G THz Indoor Communication Systems

Jun Wang (Southeast University, China); Cheng-Xiang Wang (Southeast University & Heriot-Watt University, China); Jie Huang and Haiming Wang (Southeast University, China)

0

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary massive multiple-input multiple-output (MIMO) channel model for 6G THz indoor communication systems is proposed. In this geometry-based stochastic model (GBSM), the initialization and evolution of parameters in time, space, and frequency domains are developed to generate the complete channel transfer function (CTF). Based on the proposed model, the correlation functions including time auto-correlation function (ACF), spatial cross-correlation function (CCF), and frequency correlation function (FCF) are investigated. The results show that the statistical properties of the simulation model match well with those of the theoretical model. The stationary intervals at different frequencies are simulated. The non-stationarity in time, space, and frequency domains is verified by theoretical derivations and simulations.

Measurement-based Characterization of 73GHz Propagation Channels in Scatterer-rich Environments

Zeyu Huang, José Rodríguez-Piñeiro, Xuefeng Yin and Yejian Lv (Tongji University, China); Haowen Wang (Shanghai Research Center for Wireless Communications, China)

0

The use of millimeter-wave bands is undoubtedly one of the key technologies considered by the Fifth Generation Communication Systems (5G). However, the performance of millimeter-wave communication systems can be severely affected by the propagation environment. In this paper, the propagation channel characteristics in scatterer-rich environments for the 73 GHz millimeter-wave band is studied based on measurements. The channel characteristics under study include the propagation loss and its variation depending on the elements around the receiver, the channel dispersion in delay represented by the Root- Mean-Square (RMS) channel delay spread and the multipath behavior in the form of multi-path component (MPC) clusters. The proposed models for the mentioned channel characteristics are of great value as reference for millimeter-wave 5G deployments.

Frequency-Selective Analog Beam Probing for Millimeter Wave Communication Systems

Christoph Jans (Technische Universität Dresden, Germany); Xiaohang Song (Technical University of Dresden, Germany); Wolfgang Rave (Dresden University of Technology, Germany); Gerhard P. Fettweis (Technische Universität Dresden, Germany)

3

This work focuses on the initial beam acquisition/alignment of millimeter wave (mmWave) communication systems. To detect the angle of arrival (AoA) and/or angle of departure (AoD), we propose a training protocol which probes all beamformers from a given codebook simultaneously by exploiting the sparse nature of mmWave channels. By applying a frequency- selective beam probing network, we can map each beamformer from the codebook to different frequencies and a spectral analysis at the receiver allows us to deduce favorable beamformers or AoDs. For practical reasons, we elaborate this idea of steering direction to frequency mapping for an orthogonal frequency division multiplexing (OFDM) communication system, i.e., we map each beamformer to specific pilot subcarriers. Under two different hardware designs, we investigate the feasibility of building such beamformer to frequency mappings for one additional radio frequency (RF) chain next to an existing OFDM communication system. We show that parallel beam training is able to achieve better effective transmission rates than exhaustive search in fasttime varying environments due to high temporal efficiency. This is crucial for mmWave communication systems which have access to large beamforming codebooks but suffer from short coherence times due to mobility and high spatial resolution.

Session Chair

Namyoon Lee (POSTECH, Korea (South))

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Session T1-S15

Networking Application

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Practical Framework for Beam Feature-based Physical Layer Identification in 802.11 ad/ay Networks

Shreya Gupta (University at Buffalo, USA); Zhi Sun (State University of New York at Buffalo, USA); Pu Wang (University of North Carolina at Charlotte, USA); Arupjyoti (Arup) Bhuyan (INL, USA)

0

The millimeter wave (mmWave) technologies can significantly increase the throughput and user capacity in the future wireless networks. In term of device authentication, due to the usage of highly directional communication link, new physical layer identification (PLI) mechanism based on the spatial-temporal beam features becomes available. However, it is not known how to implement the new PLI mechanism using commodity devices in multiple client scenario in wireless networks. To this end, this paper presents a practical operational framework for the new beam feature-based PLI that is compatible with 802.11ad/ay standards. The low cost of these commodity devices leads to much wider beams, multiple main lobes, and high side lobes which in turn results in frequent sector level sweep (SLS) even for a minimal level of the transmitter-receiver misalignment. The high mobility sensitivity also triggers SLS. The key idea is to utilize the mobility of the mmWave device to collect enough measurements, the beam pattern feature values, from different observation angles where the beam features are extracted. This mobility effect takes advantage of the rich spatial-temporal information of the feature to prevent the system from spoofing. We also propose a novel feature database refinement algorithm to strengthen the database against false accept/reject rates and increase the identification accuracy. The algorithm filters the noisy data collected in the presence of multiple-clients. The proposed operational framework is implemented in commodity 802.11ad/ay devices. We show that the proposed scheme can reach near 100% accuracy even with a minimal feature vector database in real-time scenarios. 1

Caching and Pricing based on Blockchain in a Cache-delivery Market

Yuanzhuo Lin (Beijing University of Posts and Telecommunications, China); Hui Tian (Beijng university of posts and telecommunications, China); Jiazhi Ren and Shaoshuai Fan (Beijing University of Posts and Telecommunications, China)

0

The cache-delivery market is generally composed of Content Provider (CP), users, and Mobile Network Operator (MNO) equipped with Base Stations (BSs). In order to deal with the dishonest problems of different parties, we build a caching- chain network based on blockchain. This network is a pure peer-to-peer system, which allows file acquisition transactions without going through a centralized issuer or controller, but attains a reliable and tamper-proof value transfer. The utilization of smart contracts protects the interests of all parties in the untrusted caching market. By reasonably distributing the reward of generating new blocks, we can motivate the MNO to allocate more resources for offloading the traffic of the CP. In addition, we use the linear regression model to predict user mobility and design the cache placement policy accordingly. Furthermore, we compare the performance of three caching algorithms through simulation. And the simulation results show that an appropriate choice of parameters can raise the CP's profit.

Network Formation Model of Bio-nanomachines Based on Directed Migration and Adhesion

Kazuki Yonekura, Tadashi Nakano, Yutaka Okaie, Takahiro Hara and Kaname Harumoto (Osaka University, Japan)

0

A bio-nanomachine network is useful in diverse applications of molecular communication. In this paper, we develop a network formation model of bio-nanomachines based on how vascular endothelial cells form a blood vessel-like structure. In our model, bio-nanomachines migrate toward each other using attractant molecules. When two bio-nanomachines move close to each other, they adhere to form a physical connection or a link between them. In this way, a group of bio-nanomachines forms a network collectively. We perform computer simulations using the model to identify critical parameter values at which a large- scale bio-nanomachine network emerges. The model developed in this paper helps us design synthetic communication networks of bio-nanomachines.

Optimizing Caching Policy and Bandwidth Allocation Towards User Fairness

Pengyu Cong and Chengjian Sun (Beihang University, China); Dong Liu (University of Southampton, United Kingdom (Great Britain)); Chenyang Yang (Beihang University, China)

0

User fairness is an important metric for cellular systems. It has been widely considered for wireless transmission when optimizing radio resource allocation but rarely considered for femto-caching. In this paper, we optimize caching and bandwidth allocation policies to improve long-term user fairness during content placement and content delivery by harnessing heterogeneous user preference. To this end, we maximize the minimal average data rate, where the average is taken over large-and small-scale channel gains as well as individual user requests. This gives rise to a complicated two-timescale optimization problem involving functional optimization. The objective function of the problem does not have closed-form expression due to unknown user preference and channel distributions, and the "variables" to be optimized include a function. To solve such a challenging problem, we first optimize bandwidth allocation policy given arbitrary caching policy, user locations and user requests, whose structure can be found. We next optimize the caching policy given the optimized bandwidth allocation policy. To handle the difficulty of unknown distributions, we resort to stochastic optimization. Simulation results show that optimizing caching policy exploiting user preference can support much higher minimal average rate than optimizing caching policy based on content popularity when user preferences are less similar. Besides, better user fairness can be achieved by optimizing caching policy than by optimizing bandwidth allocation.

Clustering-based Scenario-Aware LTE Grant Prediction

Peter Brand, Muhammad Sabih and Joachim Falk (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany); Jonathan Ah Sue (Intel Deutschland Gmbh, Germany); Jürgen Teich (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)

1

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G standards. Recent approaches for dynamic power management incorporate traffic prediction to power down components of the modem as often as possible. These predictive approaches have been shown to still provide substantial energy savings, even if trained purely on-line. However, a higher prediction accuracy could be achieved when performing predictor training off-line. Additionally, having pre-trained predictors opens up the ability to successfully employ predictive techniques also in less favorable situations such as short intervals of stable traffic patterns. For this purpose, we introduce a notion of similarity, based on which a clustering is performed to identify similar traffic patterns. For each resulting cluster, i.e., an identified traffic scenario, one predictor is designed and trained off-line. At run time, the system selects the pre-trained predictor with the lowest average shortterm false negative rate allowing for energy-efficient and highly accurate on-line prediction. Through experiments, it is shown that the presented mixed static/dynamic approach is able to improve the prediction accuracy and energy savings compared to a state-of-the-art approach by factors of up to 2 and up to 1.9, respectively.

Session Chair

Yunquan Dong (Nanjing University of Information Science and Technology, China)

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Session T1-S16

Resource Management and Optimization

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Joint Subcarrier and Power Allocation in D2D Communications Underlaying Cellular Networks

Caihong Kai, Yan Wu, Xinyue HU and Wei Huang (Hefei University of Technology, China)

1

For the high density of users and accompanying network service requirements in the cellular system, Device- to-Device (D2D) communication is a promising technology to cope with the increasing wireless traffic demands by reusing spectrum resources. In practice, the wireless signal is easy to be eavesdropped in D2D communications underlaying cellular networks, hence, ensuring a secure communication for cellular user equipments (CUEs) is an urgent and meaningful problem. In this paper, we propose a joint subcarrier and power allocation scheme for maximizing the sum data rate of D2D pairs, meanwhile protecting the CUEs against eavesdropping. Specifically, in the proposed scheme, we first quantify the security performance with the secrecy data rate, and obtain the closed- form expression for the optimal power allocation of CUEs and D2D pairs by tightening the quality of service (QoS) and secrecy rate requirement constraints of CUEs. Based on the obtained power allocation solution, by searching the optimal mapping relationship between CUEs and D2D pairs, we develop a subcarrier assignment strategy with the Hungarian algorithm to solve it, which can further enhance the sum data rate of D2D pairs. Simulation results demonstrate that the proposed scheme can significantly yield better performance than other schemes.

Cache Allocations for Consecutive Requests of Categorized Contents: Service Provider's Perspective

Minseok Choi (Jeju National University, USA); Andreas Molisch (University of Southern California, USA); Dong-Jun Han (KAIST, Korea (South)); Joongheon Kim (Korea University, Korea (South)); Jaekyun Moon (KAIST, Korea (South))

0

In wireless caching networks, a user generally has a concrete purpose of consuming contents in a certain preferred category, and requests more than one content in sequence. While most existing research on wireless content caching and delivery has focused only on one-shot requests, the popularity distribution of contents requested consecutively is definitely different from the one-shot request and has been not considered. Also, especially from the perspective of the service provider, it is advantageous for users to consume as many contents as possible. Thus, this paper proposes two cache allocation policies for categorized contents and consecutive user demands, which maximize 1) the cache hit rate and 2) the number of consecutive content consumption, respectively. Numerical results show how categorized contents and consecutive content requests have impacts on the cache allocation rule.

Multi-BS association and Pilot Allocation via Pursuit Learning

Naufan Raharya (University of Sydney, Australia); Wibowo Hardjawana (The University of Sydney, Australia); Obada Al-Khatib (University of Wollongong in Dubai, United Arab Emirates); Branka Vucetic (University of Sydney, Australia)

3

Pilot contamination (PC) interference causes an inaccurate user equipment's (UE) channel estimations and significant signal-to-interference ratio (SINR) degradations. To combat the PC effect and to maximize network spectral efficiency, pilot allocation can be combined with multi-Base Station (BS) association and then solved by using learning algorithm efficiently. However, current methods separate the pilot allocation and multi-BS association in the network. This results in suboptimal network spectral efficiency performance and can cause an outage where some UEs are not allocated pilots due to the limited availability of pilots at each BS. In this paper, we propose a multi-BS association and pilot allocation optimization via pursuit learning. Here, we design a parallel pursuit learning algorithm that decomposes the optimization function into smaller entities called learning automata. Each learning automaton computes the joint pilot allocation and BS association solution in parallel, by using the reward from the environment. Simulation results show that our scheme outperforms the existing schemes and does not cause an outage.

Group-based Multi-User Tracking in Mobile Millimeter-Wave Networks

Peng-Yu Lai (Novatek, Taiwan); Kuang-Hao (Stanley) Liu (National Cheng Kung University, Taiwan)

0

This work tackles the problem of user tracking in the multi-user scenario. User tracking is one of the key functional elements in millimeter wave (mmWave) communications that heavily rely on directional beamforming to overcome significant path loss. A naive strategy is to track users one by one but this not only introduces great overhead but also reduces the tracking update frequency when the number of users or antennas is large. In addition, user tracking based the angular information, namely angle of arrival (AoA)/angle of departure (AoD) often requires a good initial estimate and an iterative procedure to ensure the accuracy. Aiming to improve the tracking efficiency for multiple users, we propose to track multiple users simultaneously by partitioning users into groups based on the beamspace multi- input and multi-output (MIMO) channel representation. We formulate the joint user grouping and precoding as a mixed-integer nonlinear programming (MINP) problem and propose a low- complexity grouping algorithm. Simulation results demonstrate the significant improvement of the proposed multi-user tracking scheme over two existing approaches in terms of the spectral efficiency.

Spectrum Trading in Hybrid RF/FSO Communications: A Stackelberg Game Approach

Shenjie Huang (the Uinviersity of Edinburgh, United Kingdom (Great Britain)); Majid Safari (University of Edinburgh, United Kingdom (Great Britain))

3

In this work, a hybrid RF/FSO system employing a game theoretic spectrum trading process is investigated. Different from the hybrid systems in the literature, in the proposed system no RF spectrum is preallocated to the FSO link. Only under infrequent adverse weather conditions where the availability of the FSO link is severely impaired, the source borrows a portion of the licensed RF spectrum from the surrounding RF nodes. The competition among RF nodes is considered and the Stackelberg game is employed to model the spectrum trading game between the source of FSO link and the RF nodes. Using the leased spectrum, the source can establish a hybrid link containing not only the original FSO link but also an RF link to improve its throughout to the destination. Our performance analysis demonstrates that the proposed scheme improves the average capacity of the system in adverse weather conditions remarkably, thereby enhancing the availability of FSO communications.

Session Chair

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

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Session T2-S5

Cross-Layer MAC Design

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Simultaneous Transmit-Receive Multi-Channel Operation in Next Generation WLANs

Sharan Naribole, Wook Bong Lee, Srinivas Kandala and Ashok Ranganath (Samsung Semiconductor, Inc., USA)

1

The next-generation IEEE 802.11 standard project, IEEE 802.11be, is focused to meet the growing demands of applications including high throughput, low latency and high reliability. With the emergence of dual-radio end user devices (STAs) and tri-band Access Points (APs), efficient operation over multiple channels distributed over multiple bands is a key technology being discussed in IEEE 802.11be task group to achieve the desired objectives. Due to insufficient channel separation in frequency, STAs might be unable to perform simultaneous transmit and receive operations over the multiple channels in an asynchronous manner. To maximize the medium utilization of such constrained STAs participating in asynchronous multi-channel operation, we design and analyze Constraint Aware Asynchronous multi-channel operation (CA- ASYNC) protocol that includes an opportunistic 802.11 backoff resumption technique applied by the constrained STAs and multichannel busy state indication by the AP to the constrained STAs. Our results show that (a) CA-ASYNC's opportunistic backoff resumption technique significantly improves the medium utilization for constrained STAs compared to alternative strategies and (b) the multi-channel busy status indication significantly decreases the collisions due to constrained STAs and improves access delay performance.

A Differentially Private Classification Algorithm with High Utility for Wireless Body Area Networks

Xianwen Sun and Lingyun Shi (North China Electric Power University, China); Longfei Wu (Fayetteville State University, USA); Zhitao Guan (North China Electric Power University, China); Xiaojiang Du (Temple University, USA); Mohsen Guizani (Qatar University, Qatar)

1

The advancement of the wireless body area networks (WBAN) and sensor technologies allows us to collect a variety of physiological and behavioral data from human body. And appropriate application of machine learning methods can greatly promote the development of e-health. Nevertheless, the collected data contains personal privacy information. When using the machine learning methods to analyze the collected data, some information of the training data will be stored in the learning models unconsciously. To handle such information disclosure problem, we propose a differentially private classification algorithm based on ensemble decision tree with high utility for wireless body area networks. In order to improve the accuracy and stableness of classification, the bagging framework of ensemble learning is used in our algorithm. We aggregate the results of multiple private decision trees as the final classification in a weight-based voting way. For each private decision tree trained on the bootstrap samples, we offer a novel privacy budget allocation strategy that allows the nodes in larger depth to get more privacy budget, which can mitigate the problem of excessive noise introduced to leaf nodes to some extent. The better classification accuracy and stableness of this new algorithm, especially on small dataset, are demonstrated by simulation experiments.

Throughput Performance Study of Smart Antenna System in WiFi Networks

Hsin-Li Chiu, Sau-Hsuan Wu and Hsi-Lu Chao (National Chiao Tung University, Taiwan)

2

The throughput performance of smart antenna systems in WiFi networks is studied in this work. Considering a WiFi network whose access points (APs) support the switched-beam smart antenna system, a beam switching strategy which is compatible with the legacy WiFi protocol is proposed to enhance more concurrent links by allowing APs directionally sensing and accessing the wireless channel. The throughput advantage is verified by the simulation results in random deployment scenarios. More importantly, the proposed beam switching strategy provides extra design factors to adjust or balance the downlink and uplink throughput per WiFi user to meet the requirements of various applications and scenarios.

Resource Allocation and Throughput Maximization in Decoupled 5G Heterogeneous Networks

Humayun Zubair Khan (National University of Sciences and Technology & MCS CAMPUS, Pakistan); Mudassar Ali (University of Engineering and Technology Taxila, Pakistan); Muhammad Naeem (COMSATS University Islamabad, Wah Campus, Pakistan & Ryerson University, Canada); Imran Rashid (National University of Sciences and Technology, Pakistan); Adil Siddiqui (Military College of Signals, National University of Sciences and Technology, Pakistan); Muhammad Imran (National University of Sciences and Technology, Pakistan); Shahid Mumtaz (Instituto de Telecomunicações, Portugal)

1

Traditional downlink (DL)-uplink (UL) coupled cell association scheme is suboptimal solution for user association as most of the users are associated to a high powered macro base station (MBS) compared to low powered small base station (SBS) in heterogeneous network. This brings challenges like multiple interference issues, imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate DL-UL decoupled cell association scheme to address these challenges and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into a class of mixed integer non linear programming (MINLP) problem which is NP-hard and requires an exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA), with less complexity, is proposed as a solution to find near optimal solution. Extensive simulations work have been done to evaluate proposed algorithm. Results show effectiveness of proposed novel decoupled cell association scheme over traditional coupled cell association scheme in terms of users associated/attached, mitigating interference, traffic offloading to address traffic imbalances and sum-rate maximization.

Cross-Layer Analysis of Distributed Passive RFID Systems Over Faded Backscattering Links

Roberto Valentini, Piergiuseppe Di Marco and Roberto Alesii (University of L'Aquila, Italy); Fortunato Santucci (University of l'Aquila, Italy)

1

In this paper, we propose a model for the analysis of distributed passive backscattering systems for Radio Frequency Identification with physically separated illuminator and reader. The model takes into account the physical channel characteristics including shadow fading components and thresholds for powering and detection. The dynamic framed ALOHA medium access mechanism defined by the EPC Global Generation 2 standard is considered to evaluate the performance. To address the complexity of the system model, our framework is based on a semi-analytical approach that combines moment matching approximation method at channel level and Monte-Carlo approach for the medium access control dynamics. Results show the impact of deployment conditions and the relative positions among illuminator, tags, and reader on the identification performance. Interestingly, the detection of the back-scattered signal at a remote reader can improve the performance with a higher probability of capture. However, as the distance increases, the impact of shadow fading counterpoises the capture effect. The resulting trade-off is accurately described by our model.

Session Chair

Kyung-Joon Park (DGIST, Korea)

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Session T3-S13

Mesh, Relay, and Ad Hoc Networks

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Multi-Channel Delay Sensitive Scheduling for Convergecast Network

Daoud Burghal (University of Southern California, USA); Kyeong Jin Kim (Mitsubishi Electric Research Laboratories (MERL), USA); Jianlin Guo (Mitsubishi Electronic Research Laboratories, USA); Philip Orlik (Mitsubishi Electric Research Laboratories, USA); Toshinori Hori (Mitsubishi Electric Corp., Japan); Takenori Sumi (Mitsubishi Electric Corporation, Japan); Yukimasa Nagai (Mitsubishi Electric Research Laboratories, USA)

0

Motivated by an increasing interest in wireless networking in mission-critical applications, and a recent amendment of the time slotted channel hopping to IEEE 802.15.4, the multi-channel delay sensitive scheduling is investigated in the many-to-one network, which is also known as the convergecast network. In such a network, each node has data to be transmitted to a gateway through multi-hop communications. As a realistic setting, packet release time at each node is not assumed to be uniform. Under this assumption, the goal of this work is to design a scheduling scheme that minimizes the schedule length and maximum end-to-end delay, in which the former is essential for repetitive data acquisition, whereas the later improves the freshness of the acquired data. To achieve the scheduling goal, the problem is formulated as a multi-objective integer programming. To obtain a feasible solution and gain an insight into the problem, a lower bound on the schedule length is derived. Based on that, a new scheduling scheme is designed to minimize the two objectives simultaneously. Link level simulations verify the performance improvement of the proposed scheme over the existing schemes.

Secure Routing Protocol in Wireless Ad Hoc Networks via Deep Learning

Feng Hu and Bing Chen (Nanjing University of Aeronautics and Astronautics, China); Dian Shi and Xinyue Zhang (University of Houston, USA); Haijun Zhang (University of Science and Technology Beijing, China); Miao Pan (University of Houston, USA)

0

Open wireless channels make a wireless ad hoc network vulnerable to various security attacks, so it is crucial to design a routing protocol that can defend against the attacks of malicious nodes. In this paper, we first measure the trust value calculated by the node behavior in a period to judge whether the node is trusted, and then combine other QoS requirements as the routing metrics to design a secure routing approach. Moreover, we propose a deep learning-based model to learn the routing environment repeatedly from the data sets of packet flow and corresponding optimal paths. Then, when a new packet flow is input, the model can output a link set that satisfies the node's QoS and trust requirements directly, and therefore the optimal path of the packet flow can be obtained. The extensive simulation results show that compared with the traditional optimization-based method, our proposed deep learning-based approach cannot only guarantee more than 90% accuracy, but also significantly improves the computation time.

Multi-Layer Function Computation in Disorganized Wireless Networks

Fangzhou Wu (University of Science and Technology of China, China); Chen Li (University of Science And Technology of China, China); Guo Wei (University of Sci. & Tech. of China, China)

0

For future wireless networks, enormous numbers of interconnections are required, creating a disorganized topology and leading to a great challenge in data aggregation. Instead of collecting data individually, a more efficient technique, computation over multi-access channels (CoMAC), has emerged to compute functions by exploiting the signal-superposition property of wireless channels. However, the implementation of CoMAC in disorganized networks with multiple relays (hops) is still an open problem. In this paper, we combine CoMAC and orthogonal communication in the disorganized network to attain the computation of functions at the fusion center. First, to make the disorganized network more tractable, we reorganize the disorganized network into a hierarchical network with multiple layers that consists of subgroups and groups. In the hierarchical network, we propose multi-layer function computation where CoMAC is applied to each subgroup and orthogonal communication is adopted within each group. The general computation rate is derived and the performance is further improved through time allocation.

Cognitive two-way relaying with adaptive network coding

Szu-Liang Wang (Chinese Culture University, Taiwan & Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, China); Tsan-Ming Wu (Chung Yuan Christian University, Taiwan)

0

In this paper, the overlay cognitive two-way relaying is considered, and the adaptive network coding (AdNC) protocol is proposed for improving the outage performance. The AdNC protocol switches between the digital and analog network coding schemes according to the decoding probability. The outage probabilities of the primary and secondary users are derived over Nakagami-m frequency-selective fading channels. Monte Carlo simulations are provided for verifying the accuracy of the derivations. Simulation and analytical results demonstrate that the proposed AdNC protocol possesses advantages of the digital and analog network coding schemes for the considered system.

End-to-end Throughput Optimization in Multi-hop Wireless Networks with Cut-through Capability

Liu Shengbo and Liqun Fu (Xiamen University, China)

0

In-band full-duplex (FD) technique can efficiently improve the end-to-end throughput of a multi-hop network via enabling multi-hop FD amplify-and-forward relaying (cut- through) transmission. This paper investigates the optimal hop size of a cut-through transmission and spatial reuse to achieve the maximum achievable end-to-end throughput of a multi-hop network. In particular, we consider spatial reuse and establish an interference model for a string-topology multi-hop network with χ-hop cut-through transmission, and show that the maximum achievable end-to-end throughput is a function of χ and the spatial separation between two concurrently active cut-through transmissions. Through extensive numerical studies, we show that the achievable date rate of a cut-through transmission drastically decreases along with the increase of the hop size χ. Furthermore, we find that the 2-hop cut-through transmission mode can always achieve the maximum end-to-end throughput using Shannon Capacity formula if the spatial reuse is properly addressed. On the other hand, the results show that the 5-hop cut- through transmission mode can obtain the maximum end-to-end throughput with discrete channel rates when the self-interference cancellation is perfect and the hop distance is small.

SourceShift: Resilient Routing in Highly Dynamic Wireless Mesh Networks

Andreas Ingo Grohmann (TU Dresden, Germany); Frank Gabriel and Sandra Zimmermann (Technische Universität Dresden, Germany); Frank H.P. Fitzek (Technische Universität Dresden & ComNets - Communication Networks Group, Germany)

0

Wireless networks have to support an increasing number of devices with increasing demands on mobility and resilience. Mesh network routing protocols provide an elegant solution to the problem of connecting mobile nodes, due to their ability to adapt to topology changes. However, with increasing number of nodes and increasing mobility of the nodes, maintaining sufficiently recent routing information becomes increasingly challenging. Existing routing protocols fail to operate reliably in case of sudden link or node failures. In this work, we propose a new routing approach called SourceShift to resiliently handle dynamic networks in the absence of current network status information. SourceShift uses opportunistic routing and network coding, like MORE, but also makes use of link local feedback, like ExOR. We evaluate SourceShift in random network topologies with link and node failures and compare the results with the state of the art. The evaluation shows that SourceShift can ensure the delivery of the message when feasible. Additionally, the use of local feedback can improve the airtime efficiency compared to other routing protocols, even in cases without link or node failures. As a result, SourceShift requires less than half the airtime of state of the art routing protocols in more than 60% of the evaluated cases.

Session Chair

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

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Session T3-S14

Measurement and Analytics 1

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Real Entropy Can Also Predict Daily Voice Traffic for Wireless Network Users

Sihai Zhang, Junyao Guo, Tian Lan, Rui Sun and Jinkang Zhu (University of Science and Technology of China, China)

0

Voice traffic prediction is significant for network deployment optimization thus to improve the network efficiency. The real entropy based theorectical bound and corresponding prediction models have demonstrated their success in mobility prediction. In this paper, the real entropy based predictability analysis and prediction models are introduced into voice traffic prediction. For this adoption, the traffic quantification methods is proposed and discussed. Based on the real world voice traffic data, the prediction accuracy of N-order Markov models, diffusion based model and MF model are presented, among which, 25-order Markov models performs best and approach close to the maximum predictability. This work demonstrates that, the real entropy can also predict voice traffic well which broaden the understanding on the real entropy based prediction theory.

Identifying Cell Sector Clusters Using Massive Mobile Usage Records

Zhe Chen and Emin Aksehirli (DataSpark Pte Ltd, Singapore)

0

Optimizing capital expenditure (CapEx) has been an increasingly important objective in telco operators' cell planning process. Traditionally, neighbor cell relation is operationally managed and independent from capacity planning. In this paper, we present SCUT, an algorithm that uses massive mobile usage records to detect clusters of possible capacity-sharing sectors, such that capacity planning can be optimized based on coverage. SCUT analyzes shared usage to build a graph-based model of an operator's network and identifies its disjoint dense components as best-fit abstractions of clusters. Through analysis and benchmarking on real data, we demonstrate its scalability and potential to improve industry-standard site-based planning. SCUT has been deployed for a telco operator in Southeast Asia.

SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning

Ji Wang, Qi Jing, Jianbo Gao and Xuanwei Qiu (Peking University, China)

0

For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of training data is not good enough. In the real world, the quality of datasets without manually check cannot be guaranteed, even Google Play may contain malicious applications, which will cause the trained model failure. To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets. The proposed model utilizes genetic algorithm to help find the best combination of the component learners and improve robustness of the model. Our results show that the proposed approach achieves a more robust performance than other approaches in the same area.

Fine-grained Analysis and Optimization of Flexible Spatial Difference in User-centric Network

Danyang Wu (Beijing University of Posts and Telecommunications, China); Hongtao Zhang (Beijing University of Posts and Telecommunications & Key Lab of Universal Wireless Communications, Ministry of Education, China)

0

In user-centric network, traditional typical user analysis method based on spatial average results is no longer applicable due to the flexible spatial difference, which is the large fluctuations in user performance with spatial location. Especially because of power control leading to keen spatial competition, the spatial difference becomes much significantly, so that fine-grained analysis method is needed to evaluate its performance. This paper analyzes the spatial difference in user-centric network with power control through meta distribution from many different fine-grained perspectives to reveal that power control improves the performance not only in the sense of the spatial average, but also in the complete spatial distribution. Specifically, the complementary cumulative distribution function (CCDF) of the conditional transmitting success probability, the mean local delay and the 5%-tile users performance are given to depict power control effect on the individual links. This analysis provides the optimal values of the area and intensity for power control deployment in user-centric network. Numerical results show that after applying power control the users of high coverage probability can be improved at most by 38%, the mean local delay decreases by 2x and 4x gains can be obtained as for the 5%-tile user's performance.

Capacity Analysis of Distributed Computing Systems with Multiple Resource Types

Pengchao Han (Northeastern University, China); Shiqiang Wang (IBM T. J. Watson Research Center, USA); Kin K. Leung (Imperial College, United Kingdom (Great Britain))

0

In cloud and edge computing systems, computation, communication, and memory resources are distributed across different physical machines and can be used to execute computational tasks requested by different users. It is challenging to characterize the capacity of such a distributed system, because there exist multiple types of resources and the amount of resources required by different tasks is random. In this paper, we define the capacity as the number of tasks that the system can support with a given overload/outage probability. We derive theoretical formulas for the capacity of distributed systems with multiple resource types, where we consider the power of d choices as the task scheduling strategy in the analysis. Our analytical results describe the capacity of distributed computing systems, which can be used for planning purposes or assisting the scheduling and admission decisions of tasks to various resources in the system. Simulation results using both synthetic and real-world data are also presented to validate the capacity bounds.

Session Chair

Sihai Zhang (University of Science and Technology of China, P.R. China)

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Session T3-S15

Services and Applications

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

Deep Adaptation Networks Based Gesture Recognition using Commodity WiFi

Zijun Han and Lingchao Guo (Beijing University of Posts and Telecommunications, China); Zhaoming Lu (BUPT, China); Xiangming Wen (Beijing University of Posts and Telecommunications, China); Wei Zheng (BUPT, China)

0

Device-free gesture recognition plays a crucial role in smart home applications, setting human free from wearable devices and causing no privacy concerns. Prior WiFi-based recognition systems have achieved high accuracy in a static environment, but with limitations in adapting changes in environments and locations. In this paper, we propose a fine-grained deep adaptation networks based gesture recognition scheme (DANGR) using the Channel State Information (CSI). DANGR applies wavelet transformation for amplitude denoising, and conjugate calibration to remove CSI time-variant random phase offsets. A Generative Adversarial Networks (GAN) based data augmentation approach is proposed to reduce the large consumptions of data collection and the over-fitting risks caused by incomplete dataset. The distribution of CSI in various environments may be biased. In order to shrink these domains discrepancies in environments, we adopt domain adaptation based on multikernel Maximum Mean Discrepancy scheme, which matches the mean-embeddings of abstract representations across domains in a reproducing kernel Hilbert space. Extensive empirical evidence shows that DANGR yields mean 94.5% accuracy of gesture recognition confronting environmental variations, providing a promising scheme for practical and long-run implementation.

Non-intrusive leak monitoring system for pipeline within a closed space by wireless sensor network

Fang Wang and Weiguo Lin (Beijing University of Chemical Technology, China); Zheng Liu (University of British Columbia Okanagan, Canada); Liang Kong and Xianbo Qiu (Beijing University of Chemical Technology, China)

0

Non-intrusive detection is critical to protecting the integrity of pipelines. Based on the wireless sensor network, a novel leak monitoring system, composed of a computer center, a coordinator and wireless non-intrusive sensing nodes, is proposed for pipelines of closed spaces in this paper. The wireless nonintrusive sensing node with convenient installation and disassembly on the pipeline wall is designed. The proposed system can achieve signal synchronous sampling of all wireless non-intrusive sensing nodes by the coordinator wirelessly broadcasting the time information from its GPS to them, which is significant to guarantee the accuracy of the leak location. Based on the delay cross-correlation analysis, a leak location method is presented for multiple sensors. And experimental results demonstrate that the proposed system can accurately detect and locate pipeline leaks.

Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services

Muhmmad Atif Sarwar (National Chiao Tung University, Taiwan); Yousef-Awwad Daraghmi (Palestine Technical University Kadoorie, Palestine); Kuan-Wen Liu, Hong-Chuan Chi, Tsì-Uí İk and Yih-Lang Li (National Chiao Tung University, Taiwan)

2

Self-checkout systems enable retailers to reduce costs and customers to process their purchases quickly without waiting in queues. However, existing self-checkout systems suffer from design problems as they require large hardware consisting of a camera, sensors, RFID and other IoT technologies which increases the cost of such systems. Therefore, we propose a smart shopping cart with self-checkout, called iCart, to improve customer's experience at retail stores by enabling just walk out checkout and overcome the aforementioned problems. iCart is based on mobile cloud computing and deep learning cloud services. In iCart, a checkout event video is captured and sent to the cloud server for classification and segmentation where an item is identified and added to the shopping list. The Linux based cloud server contained the yolov2 deep learning network. iCart is a lightweight system of low cost solution which is suitable for the small-scale retail stores. The system is evaluated using real-world checkout video, and the accuracy of the shopping event detection and item recognition is about 97%. iCart demo can be found at URL: http://nol.cs.nctu.edu.tw/iCart/index.html.

CRED: Credibility-Enabled Social Network Based Q&A System for Assessing Answers Correctness

Imad Ali (Academia Sinica and National Tsing Hua University, Taiwan); Ronald Y. Chang (Academia Sinica, Taiwan); Cheng-Hsin Hsu (National Tsing Hua University, Taiwan)

0

In a question & answer (Q&A) system, credible users provide answers of higher correctness. However, in a distributed social network based Q&A (SNQ&A) system, an asker does not know a k-hop answerer's credibility, thus making it difficult for the asker to assess the answer correctness. Therefore, a credibility-enabled distributed SNQ&A system is crucial for determining the correctness of the answers. To this end, we propose CRED, a credibility-enabled distributed SNQ&A system, which facilitates each user to assess the correctness of the provided answers. CRED utilizes subjective logic to build interest- wise friend-to-friend credibility opinions under uncertainties. The developed opinions are then accumulated by CRED to get each user's aggregated credibility opinion, which may reflect the user's real credibility. CRED forwards a question to users with highest credibility beliefs in the question interest category. Our evaluation results show that, on average, CRED accomplishes higher success ratio, higher answer correctness, and lower answer uncertainty by 12.1%, 16.4%, and 22.2%, respectively, as compared to the best-performing baseline systems.

Maximizing Clearance Rate by Penalizing Redundant Task Assignment in Mobile Crowdsensing Auctions

Maggie E. Gendy and Ehab F. Badran (Arab Academy for Science, Technology and Maritime Transport, Egypt); Ahmad Al-Kabbany (Arab Academy for Science and Technology, Egypt)

1

This research is concerned with the effectiveness of auctions-based task assignment and management in centralized, participatory Mobile Crowdsensing (MCS) systems. During auctions, sensing tasks are matched with participants based on bids and incentives that are provided by the participants and the platform respectively. Recent literature addressed several challenges in auctions including untruthful bidding and malicious participants. Our recent work started addressing another challenge, namely, the maximization of clearance rate (CR) in sensing campaigns, i.e., the percentage of the accomplished sensing tasks. In this research, we propose a new objective function for matching tasks with participants, in order to achieve CR- maximized, reputation-aware auctions. Particularly, we penalize redundant task assignment, where a task is assigned to multiple participants, which can consume the budget unnecessarily. We observe that the less the bidders on a certain task, the higher the priority it should be assigned, to get accomplished. Hence, we introduce a new factor, the task redundancy factor, in managing auctions. Through extensive simulations under varying conditions of sensing campaigns, and given a fixed budget, we show that penalizing redundancy by giving higher priority to unpopular tasks yields significant CR increases of approximately 50%, compared to the highest clearance rates in the recent literature.

Session Chair

Ronald Y. Chang (Academia Sinica, Taiwan)

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Session T4-S7

Learning for Networks

Conference

2:00 PM — 3:30 PM KST

Local

May 27 Wed, 12:00 AM — 1:30 AM CDT

QLACO: Q-learning Aided Ant Colony Routing Protocol for Underwater Acoustic Sensor Networks

Zhengru Fang, Jingjing Wang and Chunxiao Jiang (Tsinghua University, Beijing, China); Biling Zhang (Beijing University of Posts and Telecommunications, China); Chuan Qin (Tsinghua University, China); Yong Ren (Tsinghua University, Beijing, China)

0

Recently, the technology of underwater wireless sensors networks (UWSNs) has received more attention on the exploitation of marine resources. However, underwater acoustic communication is still the only reliable means of ocean communication, which is entirely different from the terrestrial scene. In this paper, we propose Q-learning aided ant colony routing protocol (QLACO) to address the issues of energy-efficiency and link instability in UWSNs, which uses both the reward mechanism and artificial ants to determine a global optimal routing selection. QLACO uses the reward function to adapt to the dynamic underwater environment and enhance the packet delivery ratio (PDR). Moreover, we propose an anti-void mechanism to solve the void region dilemma. Simulation results show that QLACO outperforms Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR) and the depth-based protocol (DBR) in terms of PDR, energy consumption and latency.

DeepCReg: Improving Cellular-based Outdoor Localization using CNN-based Regressors

Karim Elawaad, Mohamed Ezzeldin and Marwan Torki (Alexandria University, Egypt)

0

In this paper, we propose DeepCReg, a convolutional neural network based regressor, that leverages the ubiquitous cellular data to estimate the location of the user in an outdoor environment. We formulate the problem of outdoor localization of a user as a regression problem. This formulation overcomes the limitations of other neural network based classification methods which estimates the position using a grid cell of pre-specified dimensions. We regress on the position directly which leads to better scalability when the testbed area is increased. Moreover, we introduce the usage of convolutional neural networks instead of fully connected neural networks to add more robustness to small changes in the environment. We evaluate our system on two different datasets to emphasize on the scalability of our regression approach. The testbeds are of size 0.147 km2 and 1.469 km2. Our system achieves median localization error of 2.06m and 2.82m on each dataset respectively, outperforming current state-of-the-art outdoor cellular based systems by at least 877% improvement in the median localization error.

Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module

Yuru Li (Peking University, China); Dongliang Duan (University of Wyoming, USA); Chen Chen and Xiang Cheng (Peking University, China); Liuqing Yang (Colorado State University, USA)

0

For autonomous driving of unmanned vehicles in intelligent transportation systems, multi-vehicle cooperative perception supported by vehicular networks can greatly improve the accuracy and reliability of the perception decisions. Currently, the perception decisions for a single vehicle are mostly provided by neural networks. Therefore, in order to fuse the perception decisions from multiple vehicles, the credibility of the neural network outputs needs to be studied. Among various factors, the environment is one of the most important affecting vehicles' perception decisions. In this paper, we propose a new evaluation criteria for the neural networks used in the perception module of unmanned vehicles. This criterion is termed as Environmental Sensitivity (ES), indicates the sensitivity of the network to environmental changes. We design an algorithm to quantitatively measure the ES value of different perception networks based on the extracted features. Experimental results show that our algorithm can well capture the sensitivity of the network in different environments and the ES values will be helpful to the subsequent decision fusion process.

Task Allocation for Mobile Crowdsensing with Deep Reinforcement Learning

Xi Tao and Wei Song (University of New Brunswick, Canada)

0

Mobile crowdsensing (MCS) is a new and promising paradigm of data collection in large-scale sensing and computing. A large group of users with mobile devices are recruited in a specific area to accomplish sensing tasks. An essential aspect of an MCS application is task allocation, which aims to efficiently assign sensing tasks to the recruited workers. Due to various resource and quality constraints, the MCS task allocation problem is often an NP-hard optimization problem. Traditional greedy or heuristic approaches are usually subject to performance loss in a certain degree so as to maintain tractability or accommodate special requirements such as incentive constraints. In this paper, we attempt to employ a deep reinforcement learning method to search for a more efficient task allocation solution. Specifically, we use a double deep Q-network (DDQN) to solve the task allocation problem as a path-planning problem with time windows. Our formulated problem takes into account location-dependency and time-sensitivity of sensing tasks, as well as the resource limits of workers in terms of maximum travelling distances. Simulations are conducted to compare the DDQN-based solution with two standard baseline solutions. The results show that our proposed solution outperforms the baseline solutions in terms of the platform's profit and the coverage of tasks.

Edge Caching Replacement Optimization for D2D Wireless Networks via Weighted Distributed DQN

Ruibin Li, Yiwei Zhao, Chenyang Wang and Xiaofei Wang (Tianjin University, China); Victor C.M. Leung (University of British Columbia, Canada); Xiuhua Li (Chongqing University, China); Tarik Taleb (Aalto University, Finland)

0

Duplicated download has been a big problem that affects the users' quality of service/experience (QoS/QoE) of current mobile networks. Edge caching and Device-to-Device communication are two promising technologies to release the pressure of repeated traffic downloading from the cloud. There are many researches about the edge caching policy. However, these researches have some limitations in the real scenarios. Traditional methods are lacking the self-adaptive ability in the dynamic environment and privacy issues will occur in centralized learning methods. In this paper, based on the virtue of Deep Q-Network (DQN), we propose a weighted distributed DQN model (WDDQN) to solve the cache replacement problem. Our model enables collaboratively to learn a shared predictive model. Trace-driven simulation results show that our proposed model outperforms some classical and state-of-the-art schemes.

Session Chair

Sung Whan Yoon (UNIST, Korea)

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Session T1-S17

5G Wireless Communications

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Popularity Prediction with Federated Learning for Proactive Caching at Wireless Edge

Kaiqiang Qi and Chenyang Yang (Beihang University, China)

1

File popularity prediction plays an important role in proactive edge caching. The widely-used methods for popularity prediction are based on centralized learning, which needs to collect the request information and even more personal information from users, incurring the privacy-disclosure risk. In this paper, we propose a method of predicting file popularity with federated learning to address the privacy issue, where the request data of each user for each file is only employed for local training at each user. To facilitate the popularity prediction at the base station (BS) without information disclosure as well as the supervised training of a neural network at each user, we let each user upload a weighted sum of its own preference and file popularity to the BS. The neural network is employed to predict the weighted sum at each user by training with the user's historical request records for each file. The local models and uploaded results of the users are aggregated at the BS. We show the convergence of the proposed popularity prediction method with a synthetic dataset. We use simulation results with a real dataset to show that the proposed method performs closely to the centralized learning based method in terms of caching performance.

Ambient backscatters-friendly 5G networks: creating hot spots for tags and good spots for readers

Romain Fara (Orange Labs, France); Dinh-Thuy Phan-Huy (Orange-France Telecom, France); Marco Di Renzo (Paris-Saclay University / CNRS, France)

0

In this paper, we present an ambient backscatters- friendly 5G network that creates locations with large power (hot spots) for tags and good reception locations (quiet spots or coherent spots) for readers. Tags are devices that communicate information to readers by backscattering ambient radio- frequency signals. The massive multiple input multiple output (M-MIMO) antenna and beamforming capability of the 5G network is used as follows. In a first step, a training device (separate from tags and readers) is used to send pilots and train the 5G network to perform focusing and/or nulling onto marked locations. In a second step, tags and readers are positioned onto the marked locations. The robustness of M-MIMO beamforming to slight changes in the environment is exploited. Our initial simulation results, in a multipath propagation channel environment, show that creating a hot spot on a tag improves the tag-reader range however with a low probability of detection. Creating a hot spot on a tag and a good reception spot on a reader at the same time, improves the tag-reader range with 99% probability of detection. The study also shows that beamforming does not degrade the performance of a legacy 5G communication.

A Simple Cell-Specific Beamforming Technique for Multi-Antenna Wireless Communications

Maksym A. Girnyk (Ericsson Research, Sweden); Sven O. Petersson (Ericsson AB, Sweden)

1

Next-generation wireless communication systems will rely on multiple-antenna deployments enabling the beamforming functionality. In contrast to data transmission that benefits from user-specific beamforming (i.e., focusing the radiation at a particular user terminal), broadcasting of control information over a public channel requires cell-specific beamforming (i.e., cell-wide coverage). This paper presents a simple beamforming technique for cell-specific signaling for a four-port linear antenna array, based on exploiting two orthogonal polarizations and a maximum-ratio combining receiver. For instance, it is shown that by carefully tuning the excitation weights it is possible to design radiation patterns with any half-power beamwidth in the range from that of a narrow DFT beam to that of a single antenna element, while preserving the constant-modulus property of the excitation weights, thereby assuring excellent power utilization. The paper, furthermore, shows a direct connection between the underlying beamforming technique and the well-known concept of Alamouti space-time coding.

Wireless Fingerprint Aided Spectrum Sensing in Cellular Cognitive Radio Networks

Xin Wang and Siji Chen (Chongqing University of Posts and Telecommunications, China); Bin Shen (Chongqing University of Posts and Telecommunications (CQUPT), China); Taiping Cui (Chongqing University of Posts and Telecommunications, China)

1

Apart from the received signal energy, geo-location information plays an important role in ameliorating spectrum sensing performance. In this paper, a novel wireless fingerprint (WFP) aided spectrum sensing scheme is proposed. Assisted by the wireless fingerprint database (WFPD), secondary user equipments (SUEs) first identify their locations in the cellular cognitive radio network (CCRN) and then ascertain the white licensed spectrum for opportunistic access. The SUEs can pinpoint their geographical locations via time of arrival (TOA) estimate over the signals received from their surrounding base-stations (BSs). In view of the fact that locations of the primary user (PU) transmitters are either readily known or practically unavailable, the SUEs can search the WFPD or perform support vector machine (SVM) algorithm to determine the availability of the licensed spectrum, according to the locations of themselves and the PU transmitters (PUTs). In addition, to alleviate the deficiency of single SU based sensing, a joint prediction mechanism is proposed on the basis of cooperations of multiple SUs that are geographically nearby. Simulations verify that the proposed scheme achieves higher detection probability and demands less energy consumption than conventional spectrum sensing algorithms.

Session Chair

Jiho Song (University of Ulsan, Korea (South))

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Session T1-S18

Signal Detection and Estimation

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Reconstruction Algorithm for Primary Channel Statistics Estimation Under Imperfect Spectrum Sensing

Ogeen Toma and Miguel López-Benítez (University of Liverpool, United Kingdom (Great Britain)); Dhaval Karshanbhai Patel (School of Engineering and Applied Science-Ahmedabad University, India); Kenta Umebayashi (Tokyo University of Agriculture and Technology, Japan)

0

Statistical information of primary channels has received considerable research interest in the recent years. This is due to the important role that these statistics play in improving the performance of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. Although a DSA/CR system has no initial knowledge about the statistical information of the primary channels, these statistics can be estimated from the observations of spectrum sensing. However, spectrum sensing is not perfect in the real world and sensing errors are likely to occur during DSA/CR operation, which in turn leads to incorrect estimation of primary channel statistics as well. As a result, several attempts have arisen to reconstruct the estimated periods of the primary channel occupancy patterns which are affected by the sensing errors, in order to provide more accurate estimation for the statistical information. However, all the reconstruction methods available in the literature assume the perfect knowledge of the primary users' minimum occupancy time. In this context, this work proposes the first reconstruction method that does not require any prior knowledge about the primary channel activity and inactivity patterns while achieving almost the same performance achieved by the latest reconstruction methods available in the literature, making it significantly attractive and feasible in practical implementation scenarios.

Methods for Fast Estimation of Primary Activity Statistics in Cognitive Radio Systems

Miguel López-Benítez and Ogeen Toma (University of Liverpool, United Kingdom (Great Britain)); Dhaval Karshanbhai Patel (School of Engineering and Applied Science-Ahmedabad University, India); Kenta Umebayashi (Tokyo University of Agriculture and Technology, Japan)

0

Cognitive Radio (CR) is aimed at increasing the efficiency of spectrum utilisation by allowing unlicensed users to opportunistically access licensed spectrum bands during the inactivity periods of the licensed users. CR systems can benefit from an accurate knowledge of the spectrum occupancy patterns and their statistical properties. This statistical information can be obtained by periodically monitoring (sensing) the idle/busy state of the licensed channels. However, a reliable estimation of the primary activity statistics may require long observations times. This work proposes efficient methods to reduce the observation time required to produce a reliable estimation of the primary activity statistics. Furthermore, a method enabling CR users to quantify the accuracy of the estimated statistics is also proposed. Compared to other existing approaches, the proposed methods can provide accurate estimations of the primary activity statistics in significantly shorter observation times, thus allowing CR users to quickly adapt to new unknown operating channels.

Applying Deep Neural Networks for Duty Cycle Estimation

Ahmed Al-Tahmeesschi, Kenta Umebayashi and Hiroki Iwata (Tokyo University of Agriculture and Technology, Japan); Miguel López-Benítez (University of Liverpool, United Kingdom (Great Britain)); Janne Lehtomäki (University of Oulu, Finland)

0

A pro-active spectrum usage prediction is a key technique in decision making and spectrum selection for dynamic spectrum access systems. This work focuses on the estimation of the duty cycle (DC) metric to reflect spectrum usage. The prediction is formulated as a time-series regression problem. Deep neural networks (DNNs) is selected to obtain accurate predictions of channel usage. Namely, Multilayer perceptron (MLP), Long short term memory (LSTM) and a hybrid model based on convolutional neural network followed by an LSTM (CNN-LSTM) layer are selected. The hyper-parameters selection has been optimised utilising both grid search and multi-stage grid search. Moreover, in many cases, the spectrum usage is measured on a smaller time scale from the actual required one. Hence, down-sampling and averaging is required. Averaging operation results in flattening the data and losing essential features to assist DNN to predict the channel usage. We show what is the minimum required time resolution to have a pro-active prediction system. Then, we propose utilising feature engineering to improve prediction accuracy. All the proposed DNNs approaches are trained on real-life measurements. The experimental evaluation demonstrated a high potential of DNNs to learn from previous spectrum usage and accurately predict the spectrum usage. Moreover, adding input features significantly assists the system to achieve accurate predictions in a pro-active manner.

Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications

Zhaorui Wang (The Hong Kong polytechnic University); Liang Liu (The Hong Kong Polytechnic University, China); Shuguang Cui (The Chinese University of Hong Kong, Shenzhen & Shenzhen Research Institute of Big Data, China)

0

In the intelligent reflecting surface (IRS) assisted communication systems, the acquisition of channel state information (CSI) is a crucial impediment for achieving the passive beamforming gain of IRS because of the considerable overhead required for channel estimation. Specifically, under the current beamforming design for IRS-assisted communications, KMN + KM channel coefficients should be estimated if the passive IRS cannot estimate its channels with the base station (BS) and users due to its lack of radio frequency (RF) chains, where K, N and M denote the numbers of users, reflecting elements of the IRS, and antennas at the BS, respectively. These numbers can be extremely large in practice considering the current trend of massive MIMO (multiple-input multiple-output), i.e., a large M, and massive connectivity, i.e., a large K. To accurately estimate such a large number of channel coefficients within a short time interval, we devote our endeavour in this paper to investigating the efficient pilot-based channel estimation method in IRS-assisted uplink communications. Building upon the observation that each IRS element reflects the signals from all the users to the BS via the same channel, we analytically verify that a time duration consisting of $K+N+\mathrm{max}(K+1,\lceil\frac{(K-1)N}{M}\rceil)$ pilot symbols is sufficient for the BS to perfectly recover all the KMN + KM channel coefficients for the case without receiver noise. In contrast to the conventional uplink communications without IRS in which the minimum pilot sequence length is independent with the number of receive antennas, our study reveals the significant role of massive MIMO in reducing the channel training time for IRS-assisted communications.

Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

Wang Pi and Pengtao Yang (Peking University, China); Dongliang Duan (University of Wyoming, USA); Chen Chen and Xiang Cheng (Peking University, China); Liuqing Yang (Colorado State University, USA)

0

The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.

Session Chair

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

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Session T1-S19

Energy Efficient Communications

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Minimization of Sum Inverse Energy Efficiency for Multiple Base Station Systems

Zijian Wang (Université Catholique de Louvain, Belgium); Luc Vandendorpe (Université catholique de Louvain, Belgium); Mateen Ashraf (University Catholique de Louvain, Louvain-la-Neuve, Belgium); Yuting Mou and Nafiseh Janatian (Université Catholique de Louvain, Belgium)

0

A sum inverse energy efficiency (SIEE) minimization problem is solved. Compared with conventional sum energy efficiency (EE) maximization problems, minimizing SIEE achieves a better fairness. The paper begins by proposing a framework for solving sum-fraction minimization (SFMin) problems, then uses a novel transform to solve the SIEE minimization problem in a multiple base station (BS) system. After the reformulation into a multi-convex problem, the alternating direction method of multipliers (ADMM) is used to further simplify the problem. Numerical results confirm the efficiency of the transform and the fairness improvement of the SIEE minimization. Simulation results show that the algorithm convergences fast and the ADMM method is efficient.

Joint Optimization for PS-based SWIPT Multiuser Systems with Non-linear Energy Harvesting

Thang X. Vu, Symeon Chatzinotas, Sumit Gautam and Eva Lagunas (University of Luxembourg, Luxembourg); Björn Ottersten (University of Luxembourg, Luxembourg)

0

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) multiuser systems, in which a base station serves a set of users with both information and energy simultaneously via a power splitting (PS) mechanism. To capture realistic scenarios, a nonlinear energy harvesting (EH) model is considered. In particular, we jointly design the PS factors and the beamforming vectors in order to maximize the total harvested energy, subjected to rate requirements and a total transmit power budget. To deal with the inherent non-convexity of the formulated problem, an iterative optimization algorithm is proposed based on the inner approximation method and semidefinite relaxation (SDR), whose convergence is theoretically guaranteed. Numerical results show that the proposed scheme significantly outperforms the baseline max-min based SWIPT multicast and fixed-power PS designs.

A Novel Low-Complexity Power-Allocation Algorithm for Multi-Tone Signals for Wireless Power Transfer

Boules Mouris (KTH Royal Institute of Technology, Sweden); Henrik Forssell (KTH, Sweden); Ragnar Thobaben (KTH Royal Institute of Technology, Sweden)

0

Recent studies proved that optimized multi-tone signals can significantly enhance the performance of wireless power transfer (WPT) systems. However, optimizing the power allocation for multi-tone signals in order to maximize the efficiency of WPT is a computationally complex task. In this paper, a novel low-complexity algorithm, the truncated maximum-ratio transmission (TMRT) algorithm, for allocating power to multitone signals for WPT is proposed. The algorithm exploits the fact that optimal algorithms tend to allocate power to tones having the strongest channels and no power to weaker channels, and therefore, performs maximum ratio transmission power allocation on the subset of the m strongest channels. In this way, the power allocation problem is reduced to finding the optimal m that maximizes the efficiency. Simulation results confirm that the proposed TMRT algorithm achieves a performance very close to the optimal power allocation, despite its very low complexity, and significantly outperforms other low-complexity solutions.

Towards Power-Efficient Aerial Communications via Dynamic Multi-UAV Cooperation

Lin Xiang, Lei Lei and Symeon Chatzinotas (University of Luxembourg, Luxembourg); Björn Ottersten (University of Luxembourg, Luxembourg); Robert Schober (Friedrich-Alexander University Erlangen-Nuremberg, Germany)

0

Aerial base stations (BSs) attached to unmanned aerial vehicles (UAVs) constitute a new paradigm for next- generation cellular communications. However, the flight range and communication capacity of aerial BSs are usually limited due to the UAVs' size, weight, and power (SWAP) constraints. To address this challenge, in this paper, we consider dynamic cooperative transmission among multiple aerial BSs for power- efficient aerial communications. Thereby, a central controller intelligently selects the aerial BSs navigating in the air for cooperation. Consequently, the large virtual array of moving antennas formed by the cooperating aerial BSs can be exploited for low-power information transmission and navigation, taking into account the channel conditions, energy availability, and user demands. Considering both the fronthauling and the data transmission links, we jointly optimize the trajectories, cooperation decisions, and transmit beamformers of the aerial BSs for minimization of the weighted sum of the power consumptions required by all BSs. Since obtaining the global optimal solution of the formulated problem is difficult, we propose a low-complexity iterative algorithm that can efficiently find a Karush-Kuhn-Tucker (KKT) solution to the problem. Simulation results show that, compared with several baseline schemes, dynamic multi-UAV cooperation can significantly reduce the communication and navigation powers of the UAVs to overcome the SWAP limitations, while requiring only a small increase of the transmit power over the fronthauling links.

Energy Efficiency Optimization for Secure Transmission in a MIMO-NOMA System

Miao Zhang and Kanapathippillai Cumanan (University of York, United Kingdom (Great Britain)); Wei Wang (Nantong University, China); Alister G. Burr (University of York, United Kingdom (Great Britain)); Zhiguo Ding (University of Manchester, United Kingdom (Great Britain)); Sangarapillai Lambotharan (Loughborough University, United Kingdom (Great Britain)); Octavia A. Dobre (Memorial University, Canada)

0

This paper investigates a secrecy energy efficiency (SEE) optimization problem for a multiple-input multiple-output non-orthogonal multiple access network. In particular, a multi-antenna transmitter intends to send two integrated service messages: a confidential message for the stronger user and a broadcast message for both stronger and weaker users. It is assumed that both users are equipped with multi-antennas. In this secure wireless network, we consider the transmit covariance matrices design of confidential and broadcast message, under broadcast energy efficiency (BEE) constraint. In addition, it is assumed that the weaker user might turn out to be a potential eavesdropper due to the broadcast nature of wireless transmission. We formulate this transmit covariance matrices design as an SEE maximization problem which is non-convex in its original form due the non-linear fractional objective function and constraints. To realize the solution for this problem, we utilize non-linear fractional programming and difference of concave (DC) functions approach which facilitate to reformulate it into a tractable form. Based on the Dinkelbach's algorithm and DC approximation method, we propose iterative algorithms to determine a solution to the original SEE maximization problem. Numerical results are provided to demonstrate the performance of the proposed transmit covariance matrices design to maximize the SEE.

Session Chair

Seok-Ho Chang (Konkuk University, Korea (South))

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Session T2-S6

Wireless MAC for 5G

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

UAV-Assisted Data Collection with Non-Orthogonal Multiple Access

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

0

Unmanned aerial vehicles (UAVs) facilitate information collection greatly in Internet of Things (IoT) systems. On the other hand, non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G networks. The integration of NOMA into UAV-assisted wireless networks shows great potential, but how to determine the user grouping and power allocation in NOMA according to the different locations of UAV is challenging. In this paper, we propose a general NOMA- enabled UAV-assisted data collection (NUDC) protocol to solve the formulated sum rate maximization problem such that the location of UAV, sensor grouping, and power control are jointly considered. Moreover, a joint signal-to-interference-ratio (SIR) hypergraph-based grouping and power control (SHG-PC) NOMA scheme is provided to obtain the appropriate sensor grouping and the optimal power control solutions efficiently. Extensive simulation results demonstrate the effectiveness of our proposed protocol.

HMC: A Hopping-based Multi-channel Coordination Scheme for URLLC in Unlicensed Spectrum

Hsueh-Yi Chen, Pei-Feng Lee and Te-Wei Chiang (National Central University, Taiwan); Sheng-Shih Wang (Lunghwa University of Science and Technology, Taiwan); Shiann-Tsong Sheu (National Central University, Taiwan)

0

IMT-2020 focuses on providing new services over diverse application scenarios, such as ultra-reliable low latency communications (URLLC). Regarding the increase of mobile traffic demand, the consensus of deploying 5G in unlicensed spectrum has been reached under the premise that the coexistence issue between 5G and Wi-Fi has been resolved. This paper proposes a hopping-based multi-channel coordination scheme, aiming at determining the minimal number of channels occupied for URLLC service without violating regulations. The proposed scheme uses the busy tone and full-duplex radio technologies to reduce the interference and collision. Moreover, by using the proposed scheme, the URLLC device can efficiently hop to the channel with less or no collision for data transmission. Numerical results validated that the proposed scheme actually achieves the reliability requirement of URLLC.

Graph-Based File Dispatching Protocol with D2D-Aided UAV-NOMA Communications in Large-Scale Networks

Baoji Wang (Peking University, China); Rongqing Zhang (Tongji University, China); Chen Chen and Xiang Cheng (Peking University, China); Liuqing Yang (Colorado State University, USA)

1

Unmanned aerial vehicle (UAV)-assisted communications are expected to become an important part of the next generation mobile communication systems, due to the high mobility of the UAVs. Non-orthogonal multiple access (NOMA) is regarded as a rosy technology in the fifth generation (5G) mobile communication systems, since it can effectively improve the spectral efficiency. In this paper, we combine the advantages of the UAV-assisted communications and NOMA, and propose a device-to-device (D2D)-enhanced UAV-NOMA network architecture, in which D2D is introduced to increase the file dispatching efficiency. Resource reuse based on spatial reuse is also allowed to further improve the spectral efficiency. Then, we propose a graph-based file dispatching (GFD) protocol to control the interference and minimize the UAV-assisted file dispatching mission time. Simulation results verify the advantages of our proposed D2D-enhanced UAV-NOMA network architecture and the efficiency of our designed GFD protocol.

MDP-based Resource Allocation for Uplink Grant-free Transmissions in 5G New Radio

Thilina Weerasinghe, Indika A. M. Balapuwaduge and Frank Y. Li (University of Agder, Norway); Vicente Casares-Giner (Universitat Politècnica de València, Spain)

0

The diversity of application scenarios in 5G mobile communication networks calls for innovative initial access schemes beyond traditional grant-based approaches. As a novel concept for facilitating small packet transmission and achieving ultra-low latency, grant-free communication is attracting lots of interests in the research community and standardization bodies. However, when a network consists of both grant based and grantfree based end devices, how to allocate slot resources properly between these two categories of devices remains as an unanswered question. In this paper, we propose a Markov decision process based scheme which dynamically allocates grant-free resources based on a specific reliability or priority requirement. The performance of the proposed scheme is evaluated via both analysis and simulations under various traffic arrival conditions.

Semi-Static Radio Frame Configuration for URLLC Deployments in 5G Macro TDD Networks

Ali Esswie (Nokia Bell Labs, Denmark); Klaus Pedersen (Nokia - Bell Labs, Denmark); Preben Mogensen (Nokia–Bell Labs, Research Center Aalborg, Sweden)

1

Dynamic time division duplexing (TDD) is one of the major novelties of the 5G new radio standard. It notably improves the network resource utilization with sporadic directional packet arrivals. Although, the feasibility of the ultra- reliable and low-latency communications (URLLC) within such deployments is critically challenged, mainly due to the cross-link interference (CLI). In this work, we propose a semi-static and computationally-efficient TDD radio frame adaptation algorithm for 5G macro deployments. Particularly, we first identify the quasi-static variance of the cross-cell traffic buffering performance, with various CLI co-existence conditions. Accordingly, a common radio frame pattern is dynamically estimated based on the filtered multi-cell traffic statistics. Our systemlevel simulation results show that the proposed solution achieves a highly improved URLLC outage performance, i.e., offering ~ 40% reduction gain of the achievable URLLC outage latency compared to perfect static-TDD, and approaching the optimal interference-free flexible-TDD case; though, with a significantly lower control overhead size.

Session Chair

Joohyun Lee (Hanyang University, Korea)

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Session T3-S16

Mobile Edge Computing 2

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Resource Allocation for Multi-access Edge Computing with Coordinated Multi-Point Reception

Jian-Jyun Hung and Wanjiun Liao (National Taiwan University, Taiwan); Yi-Han Chiang (Osaka Prefecture University, Japan)

0

Multi-access edge computing (MEC) has emerged as a promising platform to provide user equipments (UEs) with timely computational services through the deployed edge servers. Typically, the size of an uplink task data (e.g., images or videos) required for processing is more pronounced than that of a downlink task result, and hence MEC offloading (MECO) plays a decisive role in the efficiency of MEC systems. In the light of an unprecedented growth of UEs in next-generation mobile networks, the reception of uplink signals at base stations (BSs) can be corrupted due to potential inter-user interference. To address this issue, coordinated multi-point (CoMP) reception which enables BSs to cooperatively receive uplink signals has evolved as an effective approach to enhance the received signal qualities. In this paper, we investigate a resource allocation problem for MECO with CoMP reception and formulate it as a mixed-integer non-linear program (MINLP). To solve this problem, we leverage the concept of interference graphs to characterize uplink inter-user interference, based on which we propose a resource allocation algorithm that consists of three phases: 1) computing resource allocation, 2) subcarrier allocation and cell clustering, and 3) subcarrier reuse and cell re-clustering. The simulation results show that our proposed solution can effectively enhance the delay performance of MECO through CoMP reception as compared with existing solution approaches under various system settings.

Joint Offloading and Resource Allocation for Time-Sensitive Multi-Access Edge Computing Network

Jun-jie Yu, Mingxiong Zhao, Wen-tao Li and Di Liu (Yunnan University, China); Shao Wen Yao (National Pilot School of Software，YunNan University, China); Wei Feng (Hangzhou Dianzi University, China)

0

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data offloading is studied where mobile date can be computed at both local devices and the edge cloud with the consideration of time-sensitive tasks for users. Since the NP- hardness of the considered optimization problem, we propose an iterative algorithm to decide the proportion of data to offload and design the resource allocation strategy in a sequence. Simulation results show that the proposed algorithm achieves better performance than the reference schemes.

Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC

Jianhui Liu and Qi Zhang (Aarhus University, Denmark)

0

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks within short latency for emerging Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous services in MEC system, the task arrival interval and required execution time can vary depending on services. It is challenging to schedule computation resource for the services with stochastic arrivals and runtime at an edge server (ES). In this paper, we propose a flexible computation offloading framework among users and ESs. Based on the framework, we propose a Lyapunov-based algorithm to dynamically allocate computation resource for heterogeneous time-critical services at the ES. The proposed algorithm minimizes the average timeout probability without any prior knowledge on task arrival process and required runtime. The numerical results show that, compared with the standard queuing models used at ES, the proposed algorithm achieves at least 35% reduction of the timeout probability, and approximated utilization efficiency of computation resource to non-cause queuing model under various scenarios.

Location-Privacy-Aware Service Migration in Mobile Edge Computing

Weixu Wang, Shuxin Ge and Xiaobo Zhou (Tianjin University, China)

0

This talk does not have an abstract.

Adaptive Task Partitioning at Local Device or Remote Edge Server for Offloading in MEC

Jianhui Liu and Qi Zhang (Aarhus University, Denmark)

0

Mobile edge computing (MEC) is one of the promising solutions to process computational-intensive tasks for the emerging time-critical Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented reality (AR), autonomous vehicle. The latency can be reduced further, when a task is partitioned and computed by multiple edge servers' (ESs) collaboration. However, the state-of-the-art work studies the MEC-enabled offloading based on a static framework, which partitions tasks at either the local user equipment (UE) or the primary ES. The dynamic selection between the two offloading schemes has not been well studied yet. In this paper, we investigate a dynamic offloading framework in a multi-user scenario. Each UE can decide who partitions a task according to the network status, e.g., channel quality and allocated computation resource. Based on the framework, we model the latency to complete a task, and formulate an optimization problem to minimize the average latency among UEs. The problem is solved by jointly optimizing task partitioning and the allocation of the communication and computation resources. The numerical results show that, compared with the static offloading schemes, the proposed algorithm achieves the lower latency in all tested scenarios. Moreover, both mathematical derivation and simulation illustrate that the wireless channel quality difference between a UE and different ESs can be used as an important criterion to determine the right scheme.

Session Chair

Qi Zhang (Aarhus University, Denmark)

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Session T3-S17

Measurement and Analytics 2

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Mutation Testing Framework for Ad-hoc Networks Protocols

Anis Zarrad (University of Birmingham, United Arab Emirates); Izzat Alsmadi (Texas A&M San Antonio, USA)

0

Computing networks integrate systems, services, and users around the world. Hundreds of protocols contribute to making such process flawless. A fault in protocol design or implementation can impact many users and interrupt their tasks' workflows. Testing network protocols, whether static or dynamic, can take several approaches. Driven by the expansion of applications in different domains, in this paper, we evaluated fault-based testing techniques in testing network protocols. Out fault-based testing requirements are extracted based on network protocols' specifications. Our main goal is to test whether faultbased testing techniques can find faults or bugs that cannot be discovered by classical network protocols' testing techniques. One of the significant functional testing areas that fault-based techniques can work well in is conformance testing. They can test whether the network protocol is robust enough to validate test cases that conform with protocol specification and, on the other hand, invalidate test cases that do not show such conformance. We showed through several experiments that fault-based testing can prove conformance with less effort required through other testing approaches. Generated test scenarios serve as input for the network simulator. The quality of the test scenarios is evaluated based on three perspectives: (i) code coverage, (ii) mutation score, and (iii) testing effort. We implemented the testing framework in NS2. Experiments can be recreated using other simulation environments.

On the Performance of Multi-Gateway LoRaWAN Deployments: An Experimental Study

Konstantin Mikhaylov (University of Oulu & Solmu Technologies OY, Finland); Martin Stusek (Brno University of Technology, Czech Republic); Pavel Masek (Brno University of Technology & Member of WISLAB group, Czech Republic); Radek Fujdiak (Brno University of Technology, Czech Republic); Radek Možný (Brno Technical University, Czech Republic); Sergey Andreev (Tampere University, Finland); Jiri Hosek (Brno University of Technology, Czech Republic)

0

A remarkable progress in the Low Power Wide Area Network (LPWAN) technologies over the recent years opens new opportunities for developing versatile massive Internet of Things (IoT) applications. In this paper, we focus on one of the most popular LPWAN technologies operating in the license-exempt frequency bands, named LoRaWAN. The key contribution of this study is our unique set of results obtained during an extensive measurement campaign conducted in the city of Brno, Czech Republic. During a three-months-period, the connectivity of a public Long Range Wide Area Network (LoRaWAN) with more than 20 gateways (GWs) was assessed at 231 test locations. This paper presents an analysis of the obtained results, aimed at capturing the effects related to the spatial diversity of the GW locations and the real-life multi-GW network operation with all its practical features. One of our findings is the fact that only for 47% tested locations the GW featuring the minimum geographical distance demonstrated the highest received signal strength and signal-to-noise ratio (SNR). Also, our results captured and characterized the variations in the received signal strength indicator (RSSI) and SNR as a function of the communication distance in an urban environment, and illustrated the distribution of the spreading factors (SFs) as a result of the adaptive data rate (ADR) algorithm operation in a real-life multi-GW deployment.

Big Data Enabled Mobility Robustness Optimization for Commercial LTE Networks

Jaiju Joseph (Aalto University & Elisa Corporation, Finland); Furqan Ahmed and Tommi Jokela (Elisa Corporation, Finland); Olav Tirkkonen (Aalto University, Finland); Juho Poutanen and Jarno Niemelä (Elisa Corporation, Finland)

0

Mobility Robustness Optimization (MRO) is widely considered as an important self-organizing network (SON) use-case for tackling mobility management problems in LTE/LTE- Advanced networks. In this paper, we propose a data-driven centralized SON based MRO approach that relies on data from network configuration and performance management data sources to improve mobility performance in a fully automated manner. In particular, early and late handover statistics are used by the algorithm to make decisions regarding modification of mobility parameters. Based on performance management data from a live network, it is first observed that intra-frequency handovers provide the majority of handover problems, and that problems are predominantly cell-pair specific, not cell- specific. To increase mobility robustness, the cell individual offset configuration parameter is adjusted accordingly. The algorithm is deployed in a cluster of cells in a commercial LTE network. Results show that the algorithm is able to reduce radio link failure rates by up to 40 percent within two weeks, which underscores the potential of the proposed approach for commercial LTE networks.

A No-Reference Video Streaming QoE Estimator based on Physical Layer 4G Radio Measurements

Diogo F.M. Moura (Instituto Superior Técnico, Portugal); Marco Sousa (Instituto de Telecomunicações and Celfinet, Portugal); Pedro Vieira (Instituto de Telecomunicações and ISEL, Portugal); António J. Rodrigues (IT / Instituto Superior Técnico, Portugal); Maria Paula Queluz (Instituto Superior Técnico, Portugal)

1

With the increase in consumption of multimedia content through mobile devices (e.g., smartphones), it is crucial to find new ways of optimizing current and future wireless networks and to continuously give users a better Quality of Experience (QoE) when accessing that content. To achieve this goal, it is necessary to provide Mobile Network Operator (MNO) with real time QoE monitoring for multimedia services (e.g., video streaming, web browsing), enabling a fast network optimization and an effective resource management. This paper proposes a new QoE prediction model for video streaming services over 4G networks, using layer 1 (i.e., Physical Layer) key performance indicators (KPIs). The model estimates the service Mean Opinion Score (MOS) based on a Machine Learning (ML) algorithm, and using real MNO drive test (DT) data, where both application layer and layer 1 metrics are available. From the several considered ML algorithms, the Gradient Tree Boosting (GTB) showed the best performance, achieving a Pearson correlation of 78.9%, a Spearman correlation of 66.8% and a Mean Squared Error (MSE) of 0.114, on a test set with 901 examples. Finally, the proposed model was tested with new DT data together with the network's configuration. With the use case results, QoE predictions were analyzed according to the context in which the session was established, the radio transmission environment and radio channel quality indicators.

Monostatic Backscatter Communication in Urban Microcellular Environment Using Cellular Networks

Muhammad Usman Sheikh, Furqan Jameel, Huseyin Yigitler, Xiyu Wang and Riku Jäntti (Aalto University, Finland)

0

Backscatter communication offers a reliable and energy-efficient alternative to conventional radio systems. With the additional capability of wireless power transmission, the backscatter tags can work in a completely battery-less manner. Due to these exciting features, the researchers from academia and industry are extensively investigating its utility as an enabler of massive Internet of Things (IoT) networks. However, before reaping the benefits of ambient backscatter communications, it is necessary to evaluate its feasibility and operability for large scale networks. To do so, this research paper provides an empirical study of the real city wide deployment of monostatic wireless powered backscatter tags in Helsinki region. The coverage and outage performance has been assessed for both indoor and outdoor conditions using a sophisticated 3D ray tracing simulations. Whereby, the indoor scenario consists of several story buildings with low and high loss materials. Moreover, an in- depth evaluation of energy shortage at backscatter tags has also been provided which sheds the light on the importance of wireless power transmission for such networks. The results provided here would be helpful in upscaling the practical deployment of backscatter tags.

Session Chair

Konstantin Mikhaylov (University of Oulu, Finland)

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Session T3-S18

Vehicular Network 2

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

A Reinforcement Learning Approach for Efficient Opportunistic Vehicle-to-Cloud Data Transfer

Benjamin Sliwa and Christian Wietfeld (TU Dortmund University, Germany)

1

Vehicular crowdsensing is anticipated to become a key catalyst for data-driven optimization in the Intelligent Transportation System (ITS) domain. Yet, the expected growth in massive Machine-type Communication (mMTC) caused by vehicle-to-cloud transmissions will confront the cellular network infrastructure with great capacity-related challenges. A cognitive way for achieving relief without introducing additional physical infrastructure is the application of opportunistic data transfer for delay-tolerant applications. Hereby, the clients schedule their data transmissions in a channel-aware manner in order to avoid retransmissions and interference with other cell users. In this paper, we introduce a novel approach for this type of resource- aware data transfer which brings together supervised learning for network quality prediction with reinforcement learning- based decision making. The performance evaluation is carried out using data-driven network simulation and real world experiments in the public cellular networks of multiple Mobile Network Operators (MNOs) in different scenarios. The proposed transmission scheme significantly outperforms state-of-the-art probabilistic approaches in most scenarios and achieves data rate improvements of up to 181% in uplink and up to 270% in downlink transmission direction in comparison to conventional periodic data transfer.

Relay Selection and Coverage Analysis of Relay Assisted V2I Links in Microcellular Urban Networks

Blanca Ramos Elbal, Stefan Schwarz and Markus Rupp (TU Wien, Austria)

1

With the rising interest in vehicular communications many road safety applications have been developed over the last years. Road safety applications demand low end-to-end latency which can be supported by the large bandwidth available in the millimeter-wave (mm-wave) band. However, with growing carrier frequency the wireless network coverage degrades dramatically. In our work, we focus on enhancing the vehicle-to-infrastructure (V2I) link through idle vehicular users. We enable idle users to act as relays and to boost the signal from the Base Station (BS) to the users with poor quality links and therefore enhance the performance of the entire network. We analyze this approach in a 2-dimensional (2D) Manhattan grid where micro-cells and vehicular users are placed randomly. We consider a part of the users to be idle and select the one who maximizes the coverage improvement to boost the signal from the BS depending on the street, BS and user density. Based on techniques of stochastic geometry, we derive an analytical expression for the coverage probability of the direct link as well as the relay-assisted link and compare the analytical results to Monte Carlo system level simulations in order to validate our model.

Resource Scheduling for V2V Communications in Co-Operative Automated Driving

Prajwal Keshavamurthy (Universität Kassel, Germany); Emmanouil Pateromichelakis (Lenovo, Germany); Dirk Dahlhaus (University of Kassel, Germany); Chan Zhou (Huawei European Research Center, Germany)

0

Co-operative automated driving (CAD) use cases involve group-based vehicle-to-vehicle (V2V) communications with a wide range of quality-of-service (QoS) requirements. This work introduces and exploits fifth generation mobile networks (5G) functional architecture support for vehicle-to-everything (V2X) applications to address V2V sidelink radio resource management (RRM) for CAD use cases. A QoS requirement-aware sidelink resource allocation optimization problem is formulated for multicast group V2V communications with reliability constraints and half-duplex limitation. Furthermore, the problem is analyzed for cloud-based sidelink RRM and a dynamic vehicular environment. Accounting for the challenges in acquiring channel state information (CSI), a low-complexity scheduling scheme is presented that makes use of slowly varying large-scale channel parameters (e.g. path loss). Simulation results show significant gains in terms of packet delay performance while meeting the reliability requirements on V2V links.

Optimal Receive Beamwidth for Time Varying Vehicular Channels

Yoonseong Kang and Hyowoon Seo (KAIST, Korea (South)); Wan Choi (Seoul National University & KAIST, Korea (South))

2

This paper studies a receive beamwidth controlling method in vehicle-to-infrastructure (V2I) wireless communication system using millimeter wave (mm-wave) band. We use a triangular beam pattern to model and characterize a mm-wave receive beam pattern. First of all, channel coherence time for line-of-sight (LoS) downlink transmission is derived under the given vehicular scenario. Then, we derive an attainable data rate for the time varying vehicular channel, by supposing that the beam is realigned whenever the channel coherence time is elapsed. In addition, the optimal receive beamwidth, which achieves the maximum point of the derived attainable data rate, is obtained. The effectiveness and feasibility of the proposed receive beamwidth controlling method is underpinned by both analytic and numerical simulation results. The results are also compared with a uniform linear array (ULA) beam pattern model and show that the triangular beam pattern model can well characterize the practical antenna model.

Cluster-based Cooperative Multicast for Multimedia Data Dissemination in Vehicular Networks

Jianan Sun and Ping Dong (Beijing Jiaotong University, China); Xiaojiang Du (Temple University, USA); Tao Zheng and Yajuan Qin (Beijing Jiaotong University, China); Mohsen Guizani (Qatar University, Qatar)

0

With the development of communication technologies, vehicular network applications have evolved from basic traffic safety and efficiency applications to information and entertainment applications. The implementation of emerging vehicular applications is based on the efficient dissemination of multimedia data. In view of the dynamic topology changes, severe channel fading and limited spectrum resources of vehicular networks, how to achieve efficient multimedia data dissemination in the harsh network environment is an urgent problem. Based on the hybrid cellular-D2D vehicular network, this paper proposes a cluster-based cooperative multicast scheme. The scheme combines multicast transmission with D2D-assisted relay technology to provide high-quality data dissemination for vehicle users under limited spectrum resources. In this paper, we innovatively present a communication quality index that considers multiple performance factors and formulate the relay selection problem as the anti p-center problem in graph theory. Then we propose a heuristic method to solve the problem. The results show that the proposed scheme can effectively improve the utilization of wireless resources and the success rate of data dissemination.

Session Chair

Prajwal Keshavamurthy (Universität Kassel, Germany)

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Session T3-S19

5G

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Research Project to Realize Various High-reliability Communications in Advanced 5G Network

Takahide Murakami, Hiroyuki Shinbo, Yu Tsukamoto, Shinobu Nanba and Yoji Kishi (KDDI Research, Inc., Japan); Morihiko Tamai (Advanced Telecommunications Research Institute International, Japan); Hiroyuki Yokoyama (ATR, Japan); Takanori Hara and Koji Ishibashi (The University of Electro-Communications, Japan); Kensuke Tsuda and Yoshimi Fujii (Kozo Keikaku Engineering Inc., Japan); Fumiyuki Adachi, Keisuke Kasai and Masataka Nakazawa (Tohoku University, Japan); Yuta Seki (Panasonic Corporation & Core Element Technology Development Center, Japan); Takayuki Sotoyama (Panasonic Mobile Communications Co., Ltd., Japan)

1

We started a new research project in the "advanced 5G" era that aims at accommodating various types of communications involving current and emerging services with different data flow-level quality requirements. In this paper, the objectives and the technical aspects of the research project are introduced. We propose an architecture based on a virtualized radio access network (vRAN) that enables adaptive control of equipment resources and location of functions in the vRAN environment in accordance with spatially and temporally changing communication demands. The seven planned research items that are essential for realizing the advanced 5G network are listed as follows: blockage prediction, new radio access technologies (RATs) and their implementations with software-defined radio (SDR), adaptive interference and resource control, integration of radio and fiber resource control, highly efficient access transmission control, adaptive placement of BS functions, and quality aware traffic pattern prediction.

Low Complexity Channel Model for Mobility Investigations in 5G Networks

Umur Karabulut (Nokia Bell Labs, Technical University of Dresden, Germany); Ahmad Awada (Nokia Bell Labs, Germany); Andre N Barreto (Barkhausen Institut gGmbH, Germany & Universidade de Brasilia, Brazil); Ingo Viering (Nomor Research GmbH, Germany); Gerhard P. Fettweis (Technische Universität Dresden, Germany)

3

Millimeter-wave has become an integral part of 5G networks to meet the ever-increasing demand for user data throughput. Employing higher carrier frequencies introduces new challenges for the propagation channel such as higher path loss and rapid signal degradations. On the other hand, higher frequencies allow deployment of small-sized antenna elements that enable beamforming. To investigate user mobility under these new propagation conditions, a proper model is needed that captures spatial and temporal characteristics of the channel in beamformed networks. Current channel models that have been developed for 5G networks are computationally inefficient and lead to infeasible simulation time for most user mobility simulations. In this paper, we present a simplified channel model that captures the spatial and temporal characteristics of the 5G propagation channel and runs in feasible simulation time. To this end, coherence time and path diversity originating from fully fledged Geometry based Stochastic Channel Model (GSCM) are analyzed and adopted in Jake's channel model. Furthermore, the deviation of multipath beamforming gain from single ray beamforming gain is analyzed and a regression curve is obtained to be used in the system-level simulations. We show through simulations that the proposed simplified channel model leads to mobility results comparable to Jake's model for high path diversity. Moreover, the multi-path beamforming gain increases the interference in the system and in turn number of mobility failures.

Coexistence Management for URLLC in Campus Networks via Deep Reinforcement Learning

Behnam Khodapanah (TU Dresden, Germany); Tom Hößler (TU Dresden & Barkhausen Institut, Germany); Baris Alp Yuncu (TU Dresden, Germany); Andre N Barreto (Barkhausen Institut gGmbH, Germany & Universidade de Brasilia, Brazil); Meryem Simsek (Intel Labs & International Computer Science Institute, USA); Gerhard P. Fettweis (Technische Universität Dresden, Germany)

1

Increased usage of wireless technologies in unlicensed frequency bands inevitably increases the co-channel interference. Hence, for applications such as ultra-reliable-low- latency-communications (URLLC) in factory automation, the interference should be avoided. An intelligent coexistence management entity, which dynamically distributes the time and frequency resources, has been shown to be greatly beneficial in boosting efficiency and avoiding crippling interruptions of the wireless medium. This entity also supports multi-connectivity schemes, which are crucial for industry-level reliability requirements. The proposed governing technique of the coexistence management is a deep reinforcement learning (DRL) method, which is a model-free framework and channel allocation decisions are learned merely by interactions with the environment. The simulation results have shown that the employed method can greatly increase the reliability of the wireless network, when compared with legacy methods.

Modeling and Delay Analysis for SDN-Based 5G Edge Clouds

Ameen Chilwan (Norwegian University of Science and Technology, Norway); Yuming Jiang (Norwegian University of Science and Technology (NTNU), Norway)

0

The fifth generation (5G) mobile networks are envisioned to provide connectivity not only to mobile users but also to a wide range of other services such as enhanced mobile broadband (eMBB) and massive Internet of Things (mIoT). In order to meet the diverse requirements of these services in 5G, Software Defined Networking (SDN) has been proposed as an enabling technology for both the core cloud and the edge cloud, in addition to Network Slicing to achieve isolation among services. In this paper, an analytical model is developed for such an SDN- based edge cloud, focusing on the support of two services: eMBB and mIoT. To illustrate the use of the model, delay analysis of a switching node in the edge cloud is presented. The results show the relation between the packet delay and the underlying system parameters, such as slice density, and the impact of the SDN controller on the delay. An implication of the model, analysis and results is that they may be used for network / resource planning and admission control in 5G edge clouds to meet delay requirements of the services.

Zero-touch coordination framework for Self-Organizing Functions in 5G

Diego Fernando Preciado Rojas and Faiaz Nazmetdinov (Technische Universität Ilmenau, Germany); Andreas Mitschele-Thiel (Ilmenau University of Technology, Germany)

0

Traditional mobile network services are built by chaining together multiple functional boxes on which creation of new services is rather static. With the advent of 5G technology the ability to offer agile on-demand services to the users is mandatory. Therefore lifecycle operations such as service initial deployment, configuration changes, upgrades, scale-out, scale- in, optimization, self-healing etc. should be fully automated steps. Self-Organized Networks Functions (SF) were proposed to provide self-adaptation capabilities to mobile networks on different fronts: configuration, optimization and healing and somehow reduce the error-prone human intervention. Nevertheless, conventional design of these SFs was based on single objective optimization approaches where SFs were considered as standalone agents aiming at one very specific local objective (e.g. reduce the interference or increase the coverage). Thus, complex inter-dependencies between SFs were at some extent unattended, so when more than one function is acting on the network, conflicts are inevitable. A well-studied conflict happens when Mobility Load Balancing (MLB) and Mobility Robustness Optimization (MRO) functions are simultaneously set up: without coordination, performance degradation is expected because of the cross-dependencies between both SFs. To cope with these underlying conflicts, we propose a zero-touch coordination framework based on Machine Learning (ML) to automatically learn the dynamics between the selected SFs and assist the network optimization task.

Session Chair

Ameen Chilwan (Norwegian University of Science and Technology, Norway)

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Session T4-S8

Cellular Networks and 5G

Conference

4:00 PM — 5:30 PM KST

Local

May 27 Wed, 2:00 AM — 3:30 AM CDT

Uplink Joint Detection: From theory to practice

Mohamed Amine Dridi (Nokia Bell Labs, France); Dora Boviz (Nokia, France); Eric Renault (Institut Mines-Telecom -- Telecom SudParis & Samovar UMR CNRS 5157, France); Laurent Roullet (Nokia Bell Labs, France); Ralf Klotsche (Nokia Bell Labs, Germany)

1

Ensuring a decent Quality of Experience (QoE) is fundamental for service providers, in particular mobile networks operators, when designing their current and future connectivity solutions. With this aim in view, they are compelled to cope with potential QoE detractors such as Inter-Cell Interference (ICI) which is expected to be a liability with a foreseen network densification. This issue was anticipated in Long Term Evolution (LTE) networks and many solutions leveraging cooperation schemes between the access nodes to alleviate the ICI's effects can be found in the literature, notably for uplink (UL) transmissions which pose a greater challenge. Among the cooperative models dealing with UL ICI, Joint Detection (JD) is particularly interesting since it promises substantial throughput gains while maintaining a high spectral efficiency. However, its practical feasibility is still unclear. In the following work, we propose a platform gathering a set of architectural, functional and technical requirements to endow realistic LTE networks with JD capabilities.

Nonlinear Digital Self-interference Cancellation with SVR for Full Duplex Communication

Mikail Yilan, Huseyin Ozkan and Ozgur Gurbuz (Sabanci University, Turkey)

2

Full duplex (FD) communication has attracted significant attention due to its potential for increasing the wireless link rate twofold without increasing the occupied bandwidth. For enabling FD communication, the self-interference (SI) signal at the transmitting radio should be suppressed down to the noise level. Despite SI cancellation applied at different stages via passive, analog and digital techniques, the current methods cannot sufficiently suppress SI at all power levels. Specifically at high power levels, the nonlinear behavior of the radio should be modeled within SI cancellation. In this paper, we propose a novel nonlinear digital cancellation approach by adapting support vector regression (SVR) for FD communication. The proposed SVR based nonlinear cancellation is integrated with linear cancellation and the digital SI cancellation algorithms are implemented and tested on a software defined radio set-up integrated with a monostatic antenna. With the proposed SVR based solution, up to 5 dB enhancement in total SI suppression is observed as compared to only linear digital cancellation, for the transmit power levels higher than 20 dBm. Moreover, for the same transmit power levels, up to 3 dB higher cancellation is achieved in comparison to the memory polynomial based nonlinear digital cancellation. Incorporating the proposed solution in the FD radio design only requires changes at the algorithmic level, which is implemented in software, hence there is no need for any hardware or circuitry modification. Additionally, the proposed nonlinear solution does not cause any extra communication overhead, since SVR models are to be learned only once for each transmit power level, then stored and re-used for the later transmissions.

A Real-Time Vendor-Neutral Programmable Scheduler Architecture for Cellular Networks

Wenhao Zhang, Zhouyou Gu, Wibowo Hardjawana and Branka Vucetic (The University of Sydney, Australia); Simon Lumb and David McKechnie (Telstra Corporation Ltd., Australia); Todd Essery (Telstra, Australia)

2

The current Downlink Shared Channel (DLSCH) resource scheduler for cellular networks has the following features: 1) it is integrated with an evolved NodeB (eNB) and 2) uses proprietary interfaces. The first causes a temporary outage whenever the scheduler logic is reprogrammed to accommodate traffic profiles that have different requirements, while the latter prevents multi-vendor interoperability. In this paper, we propose a real-time vendor-neutral programmable DLSCH scheduler architecture. The scheduler and eNB are separated into two binary files that communicate via an agent. The agent uses standard interfaces to interpret information from/to different eNB vendors in real time. The proposed architecture is implemented on two open source 3rd Generation Partnership Project standard- compliant eNB stacks from the OAI and SRS. Experimental results show that the proposed architecture addresses the real time and proprietary challenges mentioned above.

User Slicing Scheme with Functional Split Selection in 5G Cloud-RAN

Salma Matoussi (LIGM-CNRS, France); Ilhem Fajjari (Orange labs, France); Nadjib Aitsaadi (UVSQ Paris Saclay, France); Rami Langar (University Gustave Eiffel, France)

2

Next Generation 5G Radio Access Network (NG-RAN) is envisioned to integrate the slicing approach to build a flexible network supporting diverse use-cases with customized architectures, features and services. RAN processing functional splits have been standardized to add new deployment design capabilities and enhance cost efficiency. A further challenge consists in how to meet the multitude use-case's requirements while considering different design models in the physical infrastructure. Current related works are tackling the slice embedding problem from a cell-centric perspective. However, to achieve greater flexibility and better resource utilization, a user-centric approach should be more exploited. In this paper, we propose a SLICE-HPSO scheme that jointly harnesses radio, processing and link resources at the user level to build multiple user slices on top of the physical infrastructure. Our proposal is tailored to different user quality-of-service requirements and to the diverse functional splits resource requests. SLICE-HPSO is in compliance with the 3GPP and optimizes further the heterogeneous resource usage while meeting the scalability requirement.

Virtual Network Function Deployment Strategy in Clustered Multi-Mobile Edge Clouds

Yijing Liu, Gang Feng and Guanqun Zhao (University of Electronic Science and Technology of China, China); Zhuo Chen (Chongqing University of Technology, China); Shuang Qin (University of Electronic Science and Technology of China, China)

1

Software Defined Networking (SDN) and Network Function Virtualization (NFV) have been widely acknowledged as the fundamental architectural technologies for 5G and beyond networks by consolidating network functions into generalpurpose hardware. Meanwhile, emerging Mobile Edge Computing (MEC) technology provides a promising solution to fulfill service requirements on high reliability and low latency, by extending cloud computing to the edge of networks. To provision a specific type of service, a virtualized network topology, such as Service Function Chain or Service Function Graph is constructed by logically connecting a set of virtual network functions (VNFs). In SDN/NFV-based mobile networks with the MEC cluster, it is imperative to develop an effective VNF deployment strategy to support various services with diverse Quality of Service requirements. In this paper, we propose a VNF deployment strategy for clustered MEC, including VNF placement and routing schemes, with the aim to minimize the average delay of service flows. We first formulate the problem as a two- dimensional knapsack problem, which is NP-Hard. To provide an efficient solution, we develop an improved genetic simulated annealing algorithm. Numerical results show that the proposed strategy can significantly reduce the end-to-end service delay in comparison with the state-of-the-art solutions.

Session Chair

Jeongho Kwak (DGIST, Korea)

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Final Program

Program at a Glance

Main Conference

Opening Session on Wednesday

WCNC 2021 Introduction

Session Chair

Keynote

6G - A Step Beyond Stretching 5G

Session Chair

Keynote

Into the Future Wireless

Session Chair

Millimeter-Wave Systems 2

Hybrid Multi-User Precoding for mmWave Massive MIMO in Frequency-Selective Channels

Secrecy Rate Analysis of mmWave MISO Ad Hoc Networks with Null Space Precoding

SINR Coverage Analysis of Dense HetNets Over Fox's H-Fading Channels

Coverage Analysis of Relay Assisted Millimeter Wave Cellular Networks with Spatial Correlation

Performance Analysis of Opportunistic Millimeter Wave Cloud-RAN with Nakagami-Blockage Channels

Session Chair

Multi-Antenna System

Time Switching Protocol for Multi-Antenna SWIPT Systems

Adaptive Antenna Array with weight and antenna space control

Channel Correlation Cancelation-Based Hybrid Beamforming for Massive Multiuser MIMO Systems

IRS-Enhanced Wideband MU-MISO-OFDM Communication Systems

MF-based Dimension Reduction Signal Compression for Fronthaul-Constrained Distributed MIMO C-RAN

Session Chair

Information Theory and Capacity

How Does Channel Coding Affect the Design of Uplink SCMA Multidimensional Constellations?

Capacity Analysis of Time-Indexed Media-based Modulation

Average Secrecy Capacity of SIMO κ-µ Shadowed Fading Channels with Multiple Eavesdroppers

LDPC-Staircase Codes for Soft Decision Decoding

On the Construction of \(G_N\)-coset Codes for Parallel Decoding

Session Chair

Resource Allocation

Effective Capacity based Resource Allocation for an Integrated Radar and Communications System

Traffic-Aware Beam Selection and Resource Allocation for 5G NR

POET: An Energy-efficient Resource Management Mechanism for One-to-Many D2D Communications

Cross-layer Resource Allocation in NOMA Systems with Dynamic Traffic Arrivals

Joint Power Allocation and Beam-forming Design for Dual-connectivity Wireless Networks

Session Chair

Mobile Edge Computing 1

Joint Computation Offloading, SFC Placement, and Resource Allocation for Multi-Site MEC Systems

Task Offloading for End-Edge-Cloud Orchestrated Computing in Mobile Networks

MeFILL: A Multi-edged Framework for Intelligent and Low Latency Mobile IoT Services

MEC-Enabled Wireless VR Video Service: A Learning-Based Mixed Strategy for Energy-Latency Tradeoff

Learning Based Fluctuation-aware Computation offloading for Vehicular Edge Computing System

Session Chair

NOMA (Non-Orthogonal Multiple Access)

Online maneuver design for UAV-enabled NOMA systems via reinforcement learning

NOMA based VR Video Transmission Exploiting User Behavioral Coherence

Joint User Association and Resource Allocation for NOMA-Based MEC: A Matching-Coalition Approach

User Scheduling and Energy Management with QoS Provisioning for NOMA-based M2M Communications

Joint Reflection Coefficient Selection and Subcarrier Allocation for Backscatter Systems with NOMA

Session Chair

Vehicular Network 1

C2RC: Channel Congestion-based Re-transmission Control for 3GPP-based V2X Technologies

A User Association Policy for UAV-aided Time-varying Vehicular Networks with MEC

Delay Sensitive Large-scale Parked Vehicular Computing via Software Defined Blockchain

Resource Allocation for AoI-Constrained V2V Communication in Finite Blocklength Regime

A Spectrum Aware Mobility Pattern Based Routing Protocol for CR-VANETs

Session Chair

Edge Computing and Caching

Overlay Coded Multicast for Edge Caching in 5G-Satellite Integrated Networks

An EPEC Analysis among Mobile Edge Caching, Content Delivery Network and Data Center

Design and Implementation on a LoRa System with Edge Computing

Collaborative Edge Computing and Caching in Vehicular Networks

Latency Guaranteed Edge Inference via Dynamic Compression Ratio Selection

Session Chair

WCNC 2020 Demo

MmWave Lens MIMO

Millimeter-Wave Massive MIMO Testbed with Hybrid Beamforming

In-Vessel Molecular MIMO Communications

Demonstration of Reconfigurable Metasurface for Wireless Communications

A Unified Platform of Free-Space Optics for High-Quality Video Transmission

Wireless VR/Haptic Open Platform for Multimodal Teleoperation

A Network Slicing Solution for Flexible Resource Allocation in SDN-based WLANs

Mobility Enhanced RPL for General Mobility Scenarios

A Reinforcement Learning based Flexible Duplex Systems for B5G with Sub-6 GHz

Experimental Study of Capture Effect in Smartphones and Wi-Fi Access Points

Latency Control for Interactive Five Degree-of-Freedom View Exploration Systems