An Improved Gauss-Seidel Algorithm For Signal Detection In Massive Mimo Systems

Massive multiple input multiple output (MIMO) is a promising technology that has been proposed to meet the requirement of the fifth generation wireless communications systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) shows near-optimal performance. However, due to the direct matrix inverse, the computational complexity of the MMSE detection algorithm is too high, especially when there are a large number of users. Thus, in this paper, we propose an improved Gauss-Seidel algorithm by utilizing delayed over relaxation (DOR) scheme, which is named as delayed over relaxation Gauss-Seidel (DRGS) algorithm. The basic idea of the DOR scheme is to combine the predicted iterative step (n + 1) with the iteration of step (n - 1). The scheme can provide a significant improvement of the convergence speed for iterative algorithm. The theoretical analysis of DRGS algorithm shows that the proposed 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 DRGS algorithm can achieve almost the same BER performance as that of MMSE detection with a small number of iterations.