Reconfigurable Intelligent Surface for Joint Sensing and Communication in Vehicular Networks Based on Deep Reinforcement Learning
Xuemei Bai, Jiarui Yuan, Chenjie Zhang, Hanping Hu, Xiaofei GengIntroduction:
Reconfigurable Intelligent Surface (RIS)-assisted Integrated Sensing and Communication (ISAC) has the potential to improve coverage and sensing capability in vehicular networks under blockage and high-mobility conditions. However, the rapid variation of vehicular channels makes joint communication and sensing resource allocation highly challenging.
Methods:
This study proposes RIS-JSC, a time–space dual-domain RIS-assisted framework that combines A/B-segment time division with RIS aperture partitioning for joint communication and sensing. A sensing reliability threshold is introduced through CFAR calibration. To solve the resulting coupled non-convex optimization problem, a Constraint-Aware Soft Actor– Critic (CA-SAC) algorithm is developed to jointly optimize time splitting, RB reuse, transmit power, RIS phase shifts, and communication/sensing sub-array control.
Results:
Simulation results show that the proposed method reduces the average V2I AoI, improves the V2V within-deadline success probability, and increases system throughput compared with SAC, PPO, TD3, DDPG, and fixed/random RIS baselines, while satisfying the sensing reliability constraint.
Discussion:
The results demonstrate that integrating time–space RIS partitioning with constraint- aware deep reinforcement learning can effectively balance communication utility, information timeliness, and sensing reliability in dynamic vehicular environments.
Conclusion:
The proposed framework provides an effective deep reinforcement learning solution for joint sensing and communication optimization in RIS-assisted vehicular networks.