Adaptive Regularized Zero-Forcing Beamforming in Massive MIMO with Multi-Antenna Users
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Modern wireless cellular networks use massive multiple-input multiple-output technology. This involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices that also use multiple antennas on their side. For this, various Beamforming and Detection techniques are used, allowing each user to receive the signal intended for him from the base station. There is an important class of linear Precoding called Regularized Zero-Forcing. In this work, we propose a special kind of regularization matrix with different regularizations for different UE, using singular values of multi-antenna users. The proposed algorithm has a simple analytical formula and is provided with theoretical study. We also show the results in comparison with other linear Precoding algorithms on simulations with the Quadriga channel model. The proposed approach leads to a significant increase in quality with the same computation time as in the baseline methods.
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