Research Area:  Vehicular Ad Hoc Networks
The ever-increasing traffic, various delay-sensitive services, and energy consumption-constrained requirements have brought huge challenges to the current communication networks in the vehicular ad-hoc networks (VANETs). These challenges motivate academia and industry to investigate novel architectures with powerful data transmission and processing capabilities for low-latency and high energy-efficiency vehicular communication. In this paper, we propose an artificial intelligence (AI) and intelligent reflecting surface (IRS) empowered energy-efficiency communication system for VANETs. First, we design a smart and efficient hybrid vehicular communication framework, where IRS-aided dedicated short-range communication and long term evolution-based cellular communication are combined for data transmission in VANETs. Secondly, an IRS-aided data transmission is proposed to improve vehicular communication, in which the head vehicles selection method is designed. Based on the direct and IRS-reflecting signal propagation, fine-grained beamforming is achieved for directional vehicular transmission. Thirdly, a deep reinforcement learning (DRL) empowered network resource control and allocation scheme is proposed. In this scheme, we formulate an energy efficiency-maximizing model under the given transmission latency for VANETs and jointly optimize the settings of all participants to achieve efficient and low-latency communication. Finally, experimental results verify the effectiveness of our proposed communication system for VANETs.
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Author(s) Name:  Qianqian Pan; Jun Wu; Jamel Nebhen; Ali Kashif Bashir; Yu Su; Jianhua Li
Journal name:  IEEE Transactions on Intelligent Transportation Systems ( Early Access )
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Publisher name:  IEEE
DOI:  10.1109/TITS.2022.3152677
Volume Information:  Page(s): 1 - 13
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9729112