A Low-Complexity Iterative Detection algorithm for Uplink Massive MIMO Systems

Linear minimum mean square error (MMSE) detector achieves nearly optimal performance for massive multiple-input multiple-output (MIMO) systems, but its complexity is significantly high because of the matrix inversion operations involved. Therefore, numerous iterative detectors along with their improved variants such as Gauss-Seidel (GS) have been introduced to tackle computational burden. In this paper, we propose an extrapolated GS (EGS)-based detector to enhance the detection performance. The proposed EGS iteratively refines the initial solution to realize near-MMSE performance without matrix inversion. It improves the convergence rate of the conventional GS detector by incorporating the extrapolation technique. It is demonstrated that the proposed algorithm outperforms the conventional GS and approaches the performance of MMSE algorithm with only a small number of iterations.