Efficient near-MMSE detector for large-scale MIMO systems

Abstract

In this paper, an improved and low-complexity signal detection approach for large-scale multiple-input multiple-output (MIMO) systems has been proposed. This approach utilizes the preconditioning technique to accelerate the conventional detection algorithm based on Gauss-Seidel (GS) iterative method, and achieves a detection performance close to the minimum mean square error (MMSE) detection algorithm with relatively small iteration counts. It also outperforms the counterparts based on the Neumann series (NS) expansion and the conjugate gradient (CG) method in poor propagation environments, such as MIMO systems with large loading or correlated factors. The corresponding architecture is also proposed with both novelty and scalability. It takes advantage of the cyclic-shift property of the GS method, and therefore facilitates the hardware implementation. Both numerical results and complexity analysis demonstrate that the proposed detector is efficient and suitable for large-scale MIMO systems.