An Accelerated Mixed Weighted-Unweighted MMSE Approach for MU-MIMO Beamforming

Precoding design based on weighted sum-rate (WSR) maximization is a fundamental problem in downlink multi-cell multi-user multiple-input multiple-output systems. While the weighted minimum mean-square error (WMMSE) algorithm is a standard solution, its high computational complexity— cubic in the number of base station antennas due to matrix inversions—hinders its application in latency-sensitive scenarios. To address this limitation, we propose a highly parallel algorithm based on a hybrid block coordinate descent framework. Our key innovation lies in updating the precoding matrix via block coordinate gradient descent, which avoids matrix inversions and relies solely on matrix multiplications, while the remaining variables are updated using block coordinate minimization. Furthermore, we introduce a two-stage warm-start strategy grounded in the sum mean-square error (MSE) minimization problem to accelerate convergence. We prove that the proposed algorithm converges to a stationary point of the WSR maximization problem. We refer to our method as the Accelerated Mixed Weighted-Unweighted Sum-MSE Minimization (A-MMMSE) algorithm. Simulation results demonstrate that A-MMMSE matches the WSR performance of both conventional WMMSE and its enhanced variant, reduced-WMMSE, while achieving a substantial reduction in computational time across diverse system configurations.