Improved Gauss–Seidel detector for large‐scale MIMO systems

Abstract

Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss-Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss-Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.

Joint steepest descent and non‐stationary Richardson method for low‐complexity detection in massive MIMO systems

“…Gauss-Seidel [29][30][31][32][33] Decomposes the MMSE filtering matrix into diagonal, upper, and lower triangular parts in order to obtain near-MMSE estimate of the transmitted signal. But, the internal sequential iterations within each GS iteration make it hard for parallel implementations.…”

“…In order to mitigate the complexity bottleneck of signal detection in massive MIMO systems, abundant works are devoted in recent years [24][25][26][27][28][29][30][31][32][33][34] to achieve MMSE performance with significantly lower complexity. Neumann series expansion (NSE) 24 based method is used to reduce the computing load by transforming the matrix inversion operation into a series of matrix-vector multiplications.…”

“…The existing RI based detectors 25,27 are overly sensitive to the relaxation parameter. 28 To achieve improved signal detection performance, Gauss-Seidel (GS) [29][30][31][32][33] based algorithms have been developed. The GS-based methods achieve good detection accuracy at low-complexity but suffer from significant correlation issues due to internal sequential iterations structure, therefore resulting in low parallelism.…”

Efficient Massive MIMO Detection for M-QAM Symbols

“…An antenna array with many antennas enables time-frequency resources to be shared by all user terminals at the same time. Massive MIMO is better than traditional MIMO in many ways, such as making links more reliable and using less energy [ 1 ]. Quadrature amplitude modulation (QAM) provides extra benefits for data transmission.…”

High-Precision Iterative Preconditioned Gauss–Seidel Detection Algorithm for Massive MIMO Systems

“…That is, its computational complexity is higher compared to the exact matrix inversion for more iterations. To further reduce the computational cost, numerous implicit methods such as the Gauss-Seidel (GS) detector [21][22][23][24], Jacobi method [25], Richardson iteration [26,27], accelerated over-relaxation (AOR) [28,29], symmetric successive over-relaxation (SSOR) [30], the Lanczos-method-based detector [31], and the conjugate gradient detector [32] have been introduced. These methods compute the estimates of the transmitted symbol without ever computing the matrix inverse.…”