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Modern wireless cellular networks use massive multiple-input multiple-output(MIMO) technology. This technology involves operations with an antenna array ata base station that simultaneously serves multiple mobile devices which alsouse multiple antennas on their side. For this, various precoding and detectiontechniques are used, allowing each user to receive the signal intended for himfrom the base station. There is an important class of linear precoding calledRegularized Zero-Forcing (RZF). In this work, we propose Adaptive RZF (ARZF)with a special kind of regularization matrix with different coefficients foreach layer of multi-antenna users. These regularization coefficients aredefined by explicit formulas based on Singular Value Decomposition (SVD) ofuser channel matrices. We study the optimization problem, which is solved bythe proposed algorithm, with the connection to other possible problemstatements. We prove theoretical estimates of the number of conditionality ofthe inverse covariance matrix of the ARZF method and the standard RZF method,which is important for systems with fixed computational accuracy. Finally, Wecompare the proposed algorithm with state-of-the-art linear precodingalgorithms on simulations with the Quadriga channel model. The proposedapproach provides a significant increase in quality with the same computationtime as in the reference methods.

Adaptive Regularized Zero-Forcing Beamforming in Massive MIMO with Multi-Antenna Users