Performance Evaluation of the Markov Chain Monte Carlo MIMO Detector based on Mutual Information

Abstract

Recently, MIMO detectors which are based on Markov Chain Monte Carlo (MCMC) simulation techniques have been proposed as alternatives to, e.g., the well-known sphere detector. In this paper, we present a systematic analysis of the performance of MCMC detectors. We study the impact of several parameters such as the list size and the number of independently running chains. As a performance criterion, our analysis is based on the mutual information over an equivalent modulation channel, rather than on coded bit error rates, because this metric is independent of the outer channel code and provides valuable insights over the whole SNR range of interest. Furthermore, we show that combining the MCMC detector with a hard-output sphere detector removes the error floor at high SNR, which is a well-known problem of the MCMC principle.