Downlink Resource Optimization in Multi-STAR-RIS-Assisted MIMO Networks

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) enables full-space manipulation of signal propagation. In this work, we consider a multi-STAR-RIS-assisted multiple-input multiple-output (MIMO) system for multi-users and investigate the impact of different STAR-RIS schemes on the system performance. We jointly optimize the beamforming matrix of the base station and the transmitting and reflecting coefficient (TRC) matrix of each STAR-RIS to maximize the sum rate of users. We propose a block coordinate descent (BCD) algorithm to optimize the beamforming matrix and the TRC matrices. We reformulate the optimization problem and optimize the beamforming matrix using the Lagrange duality method to reduce the computational complexity and then employ the constrained concave-convex procedure to tackle the optimization problem for the TRC matrices. When the energy splitting (ES) scheme is applied, our proposed method achieves a remarkable improvement of up to 17.97% in the user sum rate compared to the mode switching and the equal ES schemes in the multi-STAR-RIS-assisted system. Furthermore, when compared with systems assisted by multiple conventional RISs, our system can achieve a substantial gain of 38.99%. The improvement is even more pronounced compared to systems with a single STAR-RIS or a single conventional RIS.