Phase Shift Configuration for RIS-Based 6G Networks Using DRL Technique

Reconfigurable Intelligent Surfaces (RIS) are pivotal in enhancing the capabilities of wireless networks operating in the millimeter-wave (mmWave) and terahertz (THz) frequency bands, promising substantial improvements in the efficiency of the network. This paper investigates RIS-aided mmWave/THz networks and the configuration of the RIS reflection matrix to optimize system performance. The RIS reflection or phase shift matrix optimization presents a critical challenge due to its complex, high-dimensional nature. We therefore explore various methods to optimize the reflection matrix as benchmarks. Our approach employs Deep Reinforcement Learning (DRL), and specifically the Deep Deterministic Policy Gradient (DDPG) algorithm to formulate the optimization task to enhance network performance by effectively adjusting the RIS phase shifts. Our simulation results demonstrate that DRL effectively optimizes the RIS phase shift matrix for both low and high numbers of RIS elements. The results also show that although the DRL method has higher complexity, it performs comparably to other non-DRL methods and shows significant gains over the random phase shift method, but with the added capability to adapt to dynamic environments and to enhance the overall network performance of 6G networks.