Communications Theory

Session CT-01

Vehicular Communications

Conference
4:50 PM — 6:20 PM CST
Local
Aug 10 Mon, 4:50 AM — 6:20 AM EDT

Invited Paper: Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning

Jidong Zhang (Guangdong Communictions and Networks Institute, China); Yu Yu (South China University of Technology, China); Zhigang Wang (Guangdong Communictions and Networks Institute, China); Shaopeng Ao, Jie Tang and Xiuyin Zhang (South China University of Technology, China); Kai Kit Wong (University College London, United Kingdom (Great Britain))

0
In this paper, a UAV-assisted wireless powered communication system for IoT network is studied. Specifically, the UAV performs as base station (BS) to collect the sensory information of the IoT devices as well as to broadcast energy signals to charge them. Considering the devices' limited data storage capacity and battery life, we propose a multi-objective optimization problem that aims to minimize the average data buffer length, maximize the residual battery level of the system and avoid data overflow and running out of battery of devices. Since the services requirements of IoT devices are dynamic and uncertain and the system can not be full observed by the UAV, it is challenging for UAV to achieve trajectory planning. In this regard, a deep Q network (DQN) is applied for UAV's flight control. Simulation results indicate that the DQN-based algorithm provides an efficient UAV's flight control policy for the proposed optimization problem.

Task Offloading for Vehicular Edge Computing: A Learning-Based Intent-Aware Approach

Wenxuan Kong (North China Electric Power University, China); Lurui Jia (School of Electric and Electronic Engineering, North China Electric Power University, China); Zhenyu Zhou (North China Electric Power University & Waseda University, China); Haijun Liao (North China Electric Power University, China)

0
Air-ground integrated vehicular edge computing (AGI-VEC) has emerged as an effective solution for task processing in vehicular networks. However, due to vehicle mobility, the network topology and available computing resources vary rapidly and are difficult to predict. In this paper, we develop a novel task offloading framework for AGI-VEC, which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. Simulation results confirm that the proposed algorithm can approach the optimal performance.

Graph-based Resource Allocation For V2X Communications In Typical Road Scenarios

Yang Jiang, Shangjun Hao and Qingwen Han (Chongqing University, China)

0
As one of the ad hoc networks, VANET (Vehicular Ad-hoc Network) also faces the conflict between user numbers and resource limits. Moreover, dynamic topology features and limited frequency resources bring a representative problem, which deadly influence VANET performance. In general, two factors influence the efficiency of resource allocation. The one is system structure, while the other is a resource allocation algorithm. In this paper, a hierarchical structure, which includes fixed control layer and mobile control layer, is designed to realize allocation control, while the graph coloring method is used to allocate frequency points. Simulation results show that the proposed method could improve network performance and able to allocate resources reasonably.

Joint 3D Placement and Power Allocation for UAV-aided MIMO-NOMA Networks

Fusheng Zhu (GuangDong Communications & Networks Institute, China); Zhigang Wang (Guangdong Communictions and Networks Institute, China); Wanmei Feng, Jie Tang, Yuan Liu and Xiuyin Zhang (South China University of Technology, China)

0
This paper studies an unmanned aerial vehicle (UAV)-aided Multiple-input-Multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV acts as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices. To improve the transmission efficiency, the sum rate of all IoT devices can be maximized by jointly optimizing the three-dimensional (3D) position of the UAV, beam pattern and transmit power. To tackle this problem, we first transform the non-convex problem into a total path loss minimization problem, and then the standard convex optimization techniques is applied to obtain the optimal 3D placement of the UAV. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is proposed for achieving high steering performance of multi-beams. Finally, the closed-form expression of transmit power is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show the significant performance gains in terms of sum rate of all IoT devices can be achieved by the proposed algorithm.

Reliability Performance of Transmitter Selection in Wireless Vehicular Networks

Zhifeng Tang (The Australian National University & Australian National University, Australia); Zhuo Sun, Chunhui Li and Nan Yang (The Australian National University, Australia)

0
In this paper, we propose a novel and simple transmitter selection criterion to enhance the reliability performance of downlink transmission in a wireless vehicular network. In this network, the Manhattan-type urban street model is adopted such that the location of horizontal and vertical streets is generated by two independent and identical Poisson Point Processes (PPPs). Moreover, the location of vehicles on each street is modeled by a one-dimensional PPP and base stations (BSs) are located at the intersection of streets. According to the proposed criterion, the vehicle receives signals from either the nearest front vehicle via dedicated short range communications or the nearest front BS via cellular communications. Considering the generalized Nakagami-m fading, we derive a new easy-to-compute expression for the coverage probability of the signal-interference-plus-noise ratio of the vehicle at the origin. Aided by numerical results, we demonstrate the accuracy of the derived expression and explicitly show the performance advantage of the proposed criterion. In addition, we find that there is an optimal vehicle intensity to maximize the coverage probability.

Session Chair

Chunhui Li, Zhuo Sun

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