TY - JOUR
T1 - Artificial Intelligence-Based Energy Efficient Communication System for Intelligent Reflecting Surface-Driven VANETs
AU - Pan, Qianqian
AU - Wu, Jun
AU - Nebhen, Jamel
AU - Bashir, Ali Kashif
AU - Su, Yu
AU - Li, Jianhua
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant U21B2019 and Grant 61972255, in part by the Program of Shanghai Academic Young Research Leader under Grant 20XD1422000, and in part by the National Social Science Foundation Major Project under Grant 20&ZD140.
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - 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.
AB - 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.
KW - Intelligent reflecting surface
KW - VANETs
KW - artificial intelligence
KW - energy-efficiency communication
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U2 - 10.1109/TITS.2022.3152677
DO - 10.1109/TITS.2022.3152677
M3 - Article
AN - SCOPUS:85125726391
SN - 1524-9050
VL - 23
SP - 19714
EP - 19726
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 10
ER -