TY - JOUR
T1 - Deep Learning Enabled IRS for 6G Intelligent Transportation Systems
T2 - A Comprehensive Study
AU - Song, Wei
AU - Rajak, Shaik
AU - Chinnadurai, Sunil
AU - Dang, Shuping
AU - Liu, Ruijun
AU - Li, Jun
PY - 2022/11/1
Y1 - 2022/11/1
N2 - Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.
AB - Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.
U2 - 10.1109/TITS.2022.3184314
DO - 10.1109/TITS.2022.3184314
M3 - Article (Academic Journal)
SN - 1524-9050
VL - 24
SP - 12973
EP - 12990
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 11
ER -