A Near-Optimal Iterative Linear Precoding With Low Complexity for Massive MIMO Systems

The linear zero-forcing (ZF) precoding can achieve the near-optimal sum-rate performance when the favorable channel propagation is obtained in downlink massive multiple-input multiple-output (MIMO) systems. However, it involves high complexity with the matrix inversion. To significantly reduce the complexity of ZF precoding, we propose a weighted two-stage (WTS) precoding scheme with low complexity based on an iterative method. Specifically, the proposed WTS precoding converts the complicated matrix inversion into two-half iteration stages, and the result of each stage is weighted by a coefficient to further speed up the convergence and reduce the complexity. A theoretical analysis demonstrates that the proposed WTS precoding enjoys a fast convergence rate and low complexity. Simulation results indicate that the proposed WTS precoding can achieve better bit error rate (BER) and sum-rate performance with a smaller number of iterations than the recently proposed schemes.