An Efficient Modified Gauss Seidel Precoder for Downlink Massive MIMO Systems

Abstract

Recently, as the demand for tremendous spectral efficiency has increased, the massive multiple-input multiple-output (MIMO) system has attracted attention in the wireless communication system. In massive MIMO, the zero forcing (ZF) precoder provides optimal performance. However, the complexity for process of matrix inversion is burden in terms of practical implementation. Therefore, many researches for approximate inversion of channel matrix have been performed in order to reduce the complexity. The typical linear precoder based on approximate matrix inversion is the Gauss Seidel (GS) precoder. The GS precoder provides the similar precoded signals to ZF precoder with low complexity. However, the GS precoder does not adopt parallel implementation because of inner structure. Consequently, precoder for the GS iterative method spends a lot of times in order to estimate precoded signal. Therefore, this problem makes the GS precoder impractical. In this paper, the punctured GS (PGS) is proposed in order to mitigate the problem of parallel operation by modifying inner structure for the GS precoder. However, the performance for the PGS precoder is degraded due to modified inner structure. Therefore, the ordering PGS precoder which performance degradation due to modified inner structure is mitigated is additionally introduced. As a result, although the delay when precoded signal for the PGS precoder is obtained decreases than the GS precoder, the BER performance for the PGS precoder is degraded than the GS precoder. In contrast, the ordering PGS precoder provides improved BER performance with decrease of delay compared with the GS precoder.

Deep Unfolding of Chebyshev Accelerated Iterative method for Massive MIMO Detection

“…The second category of detection techniques for massive MIMO is based on iterative methods, in which the transmitted vector can be approximated without the need for a matrix inverse. In general, the iterative method achieves a balance between complexity and performance than the approximation inversion matrix methods [5] Examples of these methods include Jacobi (JA) [15], Gauss-Seidel (GS) [16], successive over-relaxation (SOR) [17], accelerated over-relaxation (AOR) [18], Richardson iteration (RI) [19], and conjugate gradient (CG) [20] etc. The Jacobi method [15] is a linear detection technique that can estimate the transmitted vector without requiring computationally intensive matrix inversions.…”

“…The STD algorithm significantly improves the convergence speed by providing an efficient searching direction to the Jacobi method. On the other hand, the Gauss-Seidel-based detectors [16] have a higher convergence rate than the Jacobi method. However, since the internal iterations of the Gauss-Seidel method require sequential operation, the parallel implementation of the Gauss-Seidel method is difficult.…”

“…FIGURE16. Impact of the correlated magnitude on the SER performance for 16 × 64 correlated MIMO channels with 64-QAM modulation.…”

Efficient Gauss-Seidel Precoding with Parallel Calculation in Massive MIMO Systems

“…The conventional schemes use calculation result of previous symbol for calculating present symbol. It means that conventional schemes have to wait for result of the (n − 1)th symbol to calculate the nth symbol [18]. However, parallel calculation at HGS does not need to wait for result of previous symbol.…”