Track 2 – MAC and Cross Layer Design

Session T2-S1

Multiple Access

11:00 AM — 12:30 PM KST
May 25 Mon, 10:00 PM — 11:30 PM EDT

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

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

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

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

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

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

An Adapted RFID Anti-collision Algorithm in a Dynamic Environment

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

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

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

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

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

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

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

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

Session Chair

Minseok Choi (Jeju National University, United States)

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Session T2-S2

Learning-Assisted Optimization

2:00 PM — 3:30 PM KST
May 26 Tue, 1:00 AM — 2:30 AM EDT

Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

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

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

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

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

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

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

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

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

Learning Cooperation Schemes for Mobile Edge Computing Empowered Internet of Vehicles

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

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

Distributed Deep Reinforcement Learning with Wideband Sensing for Dynamic Spectrum Access

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

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

Session Chair

Jeongho Kwak (DGIST, Korea)

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Session T2-S3

Scheduling and Radio Resource Management

4:00 PM — 5:30 PM KST
May 26 Tue, 3:00 AM — 4:30 AM EDT

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

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

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

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

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

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

Proportional Fairness through Dual Connectivity in Heterogeneous Networks

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

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

On Optimizing Signaling Efficiency of Retransmissions for Voice LTE

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

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

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

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

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

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

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

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

Session Chair

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

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