A Metropolis-Hasting-Sampling Approach for Precoding in Downlink Massive MIMO Systems

In downlink massive multiple input multiple output (MIMO) systems, matrix polynomial expansion-based (MPEB) precoder suffers either slow convergence performance or complicated computation of polynomial coefficients. To address this issue, in this paper, we proposed a novel Metropolis-Hasting (MH) Sampling-based precoder which can improve the convergence significantly with simple calculation process. The matrix polynomial coefficients of the proposed precoding are designed to boost the precision of matrix inversion approximation. The optimal polynomial coefficients can be derived by an eigenvalues estimation algorithm based on MH Sampling approach. Simulation results exhibit that compared with the benchmark approximate precodings, the proposed MH precoding is able to achieve a significant enhancement performance with lower complexity. Meanwhile, the MH precoding shows low cost and simplicity in implementation.