Deep Reinforcement Learning for Integrated Sensing and Communication in RIS-Assisted 6G V2X System

The recent advancements in integrated sensing and communications (ISACs) technology have introduced new possibilities to address the quality of communication and high-resolution positioning requirements in the next-generation wireless communication network (6G) vehicle-to-everything (V2X). Simultaneously providing high-accurate positioning and high-communication capacity (CC) for the intelligent service of the vehicle target is challenging. In this article, we propose a reconfigurable intelligent surface (RIS)-assisted 6G V2X system to achieve highly accurate positioning of the vehicle target with basic communication requirements. We provide the CC and the 3-D fisher information matrix (FIM) formulations of the vehicle target. We demonstrate the direct impact of phase modulation in the reflector units on joint positioning accuracy and CC performance. Meanwhile, we design a flexible deep deterministic policy gradient (FL-DDPG) algorithm network with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-greedy strategy to solve the high-dimensional nonconvex optimization problem, achieves minimal positioning error while satisfying various CC requirements. Simulation results demonstrate that the FL-DDPG algorithm enhances positioning accuracy by a minimum of 89% and improves the achievable rate of the vehicle target by nearly 3 times, which outperforms traditional mathematical methods. Compared with classical deep reinforcement learning methods, FL-DDPG achieves better positioning accuracy while satisfying the communication requirements. When confronting imperfect channel, FL-DDPG enables addressing the channel estimation errors effectively on the ISAC system.