Modern wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This technology involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices which also use multiple antennas on their side. For this, various precoding 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 (RZF). In this work, we propose Adaptive RZF (ARZF) with a special kind of regularization matrix with different coefficients for each layer of multi-antenna users. These regularization coefficients are defined by explicit formulas based on Singular Value Decomposition (SVD) of user channel matrices. We study the optimization problem, which is solved by the proposed algorithm, with the connection to other possible problem statements. We prove theoretical estimates of the number of conditionality of the inverse covariance matrix of the ARZF method and the standard RZF method, which is important for systems with fixed computational accuracy. Finally, We compare the proposed algorithm with state-of-the-art linear precoding algorithms on simulations with the Quadriga channel model. The proposed approach provides a significant increase in quality with the same computation time as in the reference methods.

翻译：现代无线蜂窝网络采用大规模多输入多输出（MIMO）技术。该技术涉及基站端天线阵列的操作，该阵列同时服务多个也使用多天线的移动设备。为此，采用各种预编码与检测技术，使得每个用户能从基站接收到其对应的信号。线性预编码中有一类重要方法称为正则化迫零（RZF）。本文提出了一种自适应RZF（ARZF）方法，采用特殊的正则化矩阵，该矩阵对多天线用户的每一层具有不同系数。这些正则化系数基于用户信道矩阵的奇异值分解（SVD）显式公式定义。我们研究了通过所提算法求解的优化问题，并探讨了其与其他可能问题表述的关联。我们证明了ARZF方法与标准RZF方法中逆协方差矩阵的条件数估计定理，这对于固定计算精度的系统至关重要。最后，在Quadriga信道模型仿真中，我们将所提算法与现有最先进线性预编码算法进行了比较。所提方法在计算时间与参考方法相同的情况下，显著提升了系统性能。