Capacity-approaching linear precoding with low-complexity for large-scale MIMO systems

Linear precoding techniques, such as zero forcing precoding, can achieve the near-optimal capacity due to the favorable channel propagation in large-scale MIMO systems, but involve complicated matrix inversion of large size. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. We also prove that the proposed GSbased precoding scheme has a faster convergence rate than the recently proposed Neumann-based precoding scheme. Simulation results demonstrate that the proposed scheme can achieve the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations.