Likelihood Ascent-Gibbs Sampling for efficient MIMO detection

In this paper, we propose a hybridized Likelihood Ascent-Mixed Gibbs Sampling (LAS-MGS) for effective detection with channel estimation error. We analyze its performance in the presence of channel estimation error for 2×2 and 4×4 MIMO systems employing BPSK modulation scheme. At low SNRs, performance of ZF-MGS and LAS-MGS is similar but at high SNRs, LAS-MGS performs significantly better. LAS-MGS outperforms conventional Mixed Gibbs Sampling (MGS) and we are able to harness similar gain even with channel estimation errors. We conclude that LAS-MGS is a worthy candidate for further research.