Efficient MU-MIMO Beamforming Based on Majorization-Minimization and Deep Unfolding

To release the full potentials of massive multi-user multiple-input multiple-output (MU-MIMO) for wireless communication, beamforming design is a must. In this paper, three algorithms are progressively developed for the maximization of the weighted sum-rate (WSR) problem. First, an effective beamforming algorithm is developed by applying the majorization-minimization (MM) procedure in two stages, by which the WSR problem is transformed into a series of quadratic convex problems. We prove that the proposed algorithm converges to a stationary point of the WSR. Second, to improve the efficiency of the two-stage beamforming algorithm, the inherent low-dimensional structure within the beamforming update is exploited aiming to reduce the computational complexity of the matrix inversion. Third, to further reduce the complexity, a deep unfolding beamforming network is developed, which unfolds the improved beamforming algorithm into a layer-wise structure and employs a trainable module structured on the dynamics developed to approximate the matrix inversion. Experimental results demonstrate that the proposed algorithms perform significantly better than the classical weighted minimum mean square error (WMMSE) beamforming and state-of-the-art deep unfolding beamformers in terms of sum-rate and require significantly less CPU time.