【Author】
Noor-A-Rahim, Md; Liu, Zilong; Lee, Haeyoung; Khyam, Mohammad Omar; He, Jianhua; Pesch, Dirk; Moessner, Klaus; Saad, Walid; Poor, H. Vincent
【Source】PROCEEDINGS OF THE IEEE
【Abstract】We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
【Keywords】Blockchain; brain-controlled vehicle (BCV); federated learning; intelligent reflective surfaces (IRSs); machine learning (ML); nonorthogonal multiple access (NOMA); quantum; radio frequency (RF)-visible light communication (VLC) vehicle-to-everything (V2X); sixth-generation (6G)-V2X; tactile-V2X; te
【文献类型】Article; Early Access
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