Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multi-User Large-Scale MIMO Systems

TL;DR

This paper introduces a low-complexity iterative signal detection method for uplink large-scale MIMO systems using the Gauss-Seidel approach, significantly reducing computational cost while maintaining near-optimal performance.

Contribution

It proposes a Gauss-Seidel based iterative detection algorithm with a diagonal-approximate initial solution, reducing complexity from O(K^3) to O(K^2) and achieving near-MMSE performance.

Findings

Reduces computational complexity from O(K^3) to O(K^2)

Achieves near-MMSE detection performance with few iterations

Outperforms Neumann series approximation algorithm

Abstract

For uplink large-scale MIMO systems, minimum mean square error (MMSE) algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose to exploit the Gauss-Seidel (GS) method to iteratively realize the MMSE algorithm without the complicated matrix inversion. To further accelerate the convergence rate and reduce the complexity, we propose a diagonal-approximate initial solution to the GS method, which is much closer to the final solution than the traditional zero-vector initial solution. We also propose a approximated method to compute log-likelihood ratios (LLRs) for soft channel decoding with a negligible performance loss. The analysis shows that the proposed GS-based algorithm can reduce the computational complexity from O(K^3) to O(K^2), where K is the number of users. Simulation results verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.