Weighted Gauss-Seidel Precoder for Downlink Massive MIMO Systems

In this paper, a novel precoding scheme based on the Gauss-Seidel (GS) method is proposed for downlink massive multiple-input multiple-output (MIMO) systems. The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process. In addition, the GS method shows a fast convergence rate to the Zero-forcing (ZF) method that requires an exact invertible matrix. However, to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels, more iterations are necessary for the GS method and increase the overall complexity. For efficient approximation with fewer iterations, this paper proposes a weighted GS (WGS) method to improve the approximation accuracy of the GS method. The optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square (LS) method. After the computation of weights, the different weights are applied for each iteration of the GS method. In addition, an efficient method of weight computation is proposed to reduce the complexity of the LS method. The simulation results show that bit error rate (BER) performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.

Automated summary

A novel precoding scheme based on the Gauss-Seidel method is proposed for downlink massive MIMO systems, with a weighted GS method being introduced to improve approximation accuracy and reduce complexity.