Random Subsampling based Signal Detection for Spatial Correlated Massive MIMO Channels

Abstract

Massive MIMO systems have become more popular in wireless communications due to their improved spectral efficiency compared to existing small-scale MIMO systems. However, current estimation methodes take too long for larger numbers of antennas. In this paper, a near-optimal iterative linear signal detection for massive MIMO is introduced exploiting the random projection method to approximate the channel matrix in a significantly lower dimensional space. This is then used as a preconditioner in the conjugate gradient least squares algorithm to enhance the convergence rate. For evaluation, different scenarios of spatial correlation in a massive MIMO system are considered. In contrast to other low-complexity signal detectors, our approach achieves excellent results in terms of robustness and determined latency.