An Adaptive Full-Duplex Deep Reinforcement Learning-Based Design for 5G-V2X Mode 4 VANETs

This paper exploits full-duplex (FD) technology and deep reinforcement learning (DRL) algorithm jointly and adaptively to enhance the performance of 5G-V2X networks that operate based on the 5G-V2X Mode 4 standard. Specifically, we propose a novel physical- (PHY) and medium access control- (MAC) layer cross-layer design, in which collision detection capability is enabled during broadcasting without introducing redundant signalling. Besides, the resource reservation scheme, collision resolution mechanism and scheduling policy are also designed. As the proposed adaptive method is fully decentralised, vehicular users adapt to the unknown and fast-changing environment autonomously without any help from gNBs. Simulation results demonstrate the superiority of our proposed design over the standardised sensing-based semi-persistent scheduling (SB-SPS) protocol. Therefore, the proposed cross-layer design can be considered as a solution for future 5G-V2X VANETs.