Reinforcement Learning-based Resource Allocation Scheme of NR-V2X Sidelink for Joint Communication and Sensing

Joint communication and sensing(JCS) is becoming an important trend in 6G, owing to its the efficient utilization of spectrum and hardware resource. Utilizing echoes of the same signal can achieve the object location sensing function, in addition to V2X communication function. There is application potential for JCS systems in the fields of ADAS and unmanned autos. Currently, NR-V2X sidelink has been standardized by 3GPP to support the low-latency high-reliability direct communication. In order to combine benefits from both direct communication and JCS, it is promising to extend existing NR-V2X sidelink communication towards sidelink JCS. However, the conflicting performance requirements arise between radar sensing accuracy and communication reliability with the limited sidelink spectrum. In order to overcome the challenges in the distributed resource allocation of sidelink JCS with a full-duplex, this paper has proposed a novel consecutive-collision mitigation semi-persistent scheduling (CCM-SPS) scheme, including the collision detection and Q-learning training stages to suppress collision probabilities. Theoretical performance analyses on Cramér-Rao lower bounds(CRLB) has been made for the sensing of sidelink JCS. Key performance metrics such as CRLB, PRR and UD have been evaluated. The simulation results indicate the superior performance of CCM-SPS to the counterparts, with promising application prospects.