Beamforming and Phase Design for Active RIS-Aided Communications Using GBML

In this letter, we investigate a multi-user communication network aided by active reconfigurable intelligent surface (RIS). To maximize the weighted sum rate (WSR) of the system, we formulate a joint transmit precoding and RIS coefficient optimization problem. However, the traditional alternating direction method of multipliers (ADMM) optimization approach for the problem suffers from high computational complexity and can only able to obtain suboptimal solutions. To deal with this issue, we propose a gradient-based meta-learning (GBML) scheme, which fuses meta-learning, neural network and nested iteration to improve the overall spectral efficiency and reduce the computational complexity associated with high-dimensional variables. Simulation results indicate that the proposed scheme can improve the WSR by up to 10.6%/70.25% in ideal/non-ideal scenarios compared with ADMM, and achieve a faster convergence rate as well.