Near-Optimal Linear Precoding with Low Complexity for Massive MIMO.

摘要: Linear precoding techniques can achieve near- optimal capacity due to the special channel property in down- link massive MIMO systems, but involve high complexity since complicated matrix inversion of large size is required. In this paper, we propose a low-complexity linear scheme based on Gauss-Seidel (GS) method. The proposed capacity-approaching performance classical schemes an iterative way without inversion, which reduce overall by one order magnitude. guarantee GS-based analyzed from following three aspects. At first, prove that satisfies transmit power constraint. Then, enjoys faster convergence rate than recently Neumann-based precoding. last, achieved quantified, reveals converges with increasing number BS antennas. To further accelerate and complexity, zone-based initial solution precoding, much closer final traditional solution. Simulation results demonstrate outperforms Neumann- achieves exact only small iterations both Rayleigh fading channels spatially correlated channels.