Deep Reinforcement Learning for RIS-Empowered High-Speed Railway Cell-Free Networks

Abstract

Cell-free multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) have been envisioned as two promising techniques to enhance the data transmission rate of high-speed railway (HSR) networks. This letter considers the HSR cell-free MIMO system empowered by RIS with finite discrete phase shifters to pursue performance improvement. Particularly, the RIS phase shift optimization problem is formulated, aiming at maximizing the achievable rate. To deal with the complicated control problem, a deep reinforcement learning (DRL)-based scheme is proposed, where double deep Q-network (DDQN) method is invoked for designing phase shifts. Simulation results demonstrate that compared with the existing optimization-based baseline scheme, the proposed scheme can obtain the comparable achievable rate with much shorter time consumption.