Joint BS and Beyond Diagonal RIS Beamforming Design with DRL Methods for mmWave 6G Mobile Communications

In this paper, a novel beyond-diagonal RIS (BD-RIS) is suggested an architecture to improve the spectral efficiency (SE) of wireless communication systems. We use deep reinforcement learning (DRL) to solve the joint design problem of the RIS phase shift matrix and BS beamforming to maximize the SE. In addition to the phase of each element, we also optimize the position of non-diagonal elements in the RIS phase shift matrix. Simulation results show that the proposed BD- RIS architecture with DRL outperforms the conventional diagonal RIS (D-RIS) architecture with DRL in terms of SE. We also investigate the effect of the number of quantization bits on the performance of the DRL algorithm. We show that there is a trade-off between accuracy and complexity.