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For uplink large-scale multiple-input-multiple-output (MIMO) systems, the 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 an approximated method to compute log-likelihood ratios 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 …