A Low-Complexity Linear Precoding Scheme Based on SOR Method for Massive MIMO Systems

Conventional linear precoding schemes in massive multiple-input-multiple-output (MIMO) systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size. To solve this problem, we propose a successive overrelaxation (SOR)-based precoding scheme to approximate the matrix inversion by exploiting the asymptotically orthogonal channel property in massive MIMO systems. The proposed SOR-based precoding can reduce the complexity by about one order of magnitude, and it can also approach the classical RZF precoding with negligible performance loss. We also prove that the proposed SOR-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. In addition, to guarantee the performance of SOR-based precoding, we propose a simple way to choose the optimal relaxation parameter in practical massive MIMO systems. Simulation results verify the advantages of SOR-based precoding in convergence rate and computational complexity in typical massive MIMO configurations.