Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 24, Issue 11, Pages 12973-12990Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3184314
Keywords
6G mobile communication; Channel estimation; MIMO communication; Optimization; Wireless networks; Deep learning; Array signal processing; Intelligent transportation systems (ITS); intelligent reflecting surface (IRS); deep learning (DL); 6G communications; spectral efficiency; energy efficiency
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Intelligent Transportation Systems (ITS) are increasingly important in our lives, and sixth-generation (6G) communication technologies play a crucial role in supporting safe and effective vehicular networks. Studying vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications is necessary for implementing ITS with secure, robust, and efficient massive connectivity in vehicular communications networks. Intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS as a novel paradigm that can intelligently configure incident signals between vehicles. Understanding the latest research efforts, differences with existing alternatives, and new challenges in implementing IRS in 6G ITS is key.
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.
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