Enhancing V2X Communication with Active RIS: A MADRL Approach with Perfect and Imperfect CSI

In this work, we explore the use of active reconfigurable intelligent surfaces (A-RIS) to improve vehicle-to-everything (V2X) communication systems to address limitations in traditional vehicular communication. In particular, we formulate an optimization problem to maximize the uplink sum rate for vehicle-to-infrastructure (V2I) links by optimizing transmit precoders, phase-shift matrices, transmit power, and spectrum sharing for vehicle-to-vehicle (V2V) links. To handle complex hybrid control scenarios, we propose a mixed-action deep reinforcement learning (DRL) algorithm and compare it with conventional benchmark methods like deep deterministic policy gradient (DDPG) with discrete actions (DA) and alternating optimization (AO). We evaluate the proposed algorithm’s effectiveness under imperfect channel state information as well. Simulation results highlight the efficacy of our approach, demonstrating significant enhancement in vehicular communication quality through A-RIS. Furthermore, we illustrate the impact of various factors such as number of A-RIS elements, vehicle speed, loss, execution time, amplification power, and CSI error on the performance of the V2X system.