Deep reinforcement learning-based optimization of reconfigurable intelligent surfaces (RIS) for 6G multi-user connectivity in NLOS environments

Abstract The transition towards the sixth generation (6G) of wireless networks requires ultra-high data rates and seamless connectivity, even in frequency bands like mmWave and THz, which are highly susceptible to physical blockages. RIS have emerged as a key technology in mitigating NLOS limitations through programmable steering of electromagnetic waves by passive reflection. However, real-time optimization of high-dimensional RIS phase shifts in dynamic multi-user environments remains an NP-hard challenge that is hardly solvable efficiently by traditional methods based on mathematical optimization. This paper presents a new DRL framework, utilizing a TD3 architecture, for the optimization of sum-rate performance of multi-user links. The model incorporates real-time environmental feedback and imperfect CSI for ensuring robust connectivity. Simulation results show that the DRL-RIS framework proposed here achieves remarkable gains in the sum-rate performance and outperforms conventional convex optimization baselines in computational latency and energy efficiency. Keywords: 6G Networks, Reconfigurable Intelligent Surfaces (RIS), Deep Reinforcement Learning (DRL), Non-Line-of-Sight (NLOS), Beamforming