A Capacity Achieving MIMO Detector Based on Stochastic Sampling

Abstract

Spatial-multiplexing multiple-input multiple-output (MIMO) systems have been developed and enhanced over the past two decades. In particular, a great amount of effort has gone towards development of capacity achieving detectors with affordable computational complexity. The developed detectors may be broadly divided into two classes: (i) deterministic sampling, such as list sphere decoding detector; and (ii) stocastic sampling, such as those based on Markov chain Monte Carlo (MCMC) search schemes. This paper proposes a novel detection scheme that is based on stochastic sampling, but is fundamentally different from the MCMC detectors. While MCMC follows a set of sequential sampling steps, hence, the sample sets obtained are highly correlated, the method proposed in this paper takes stochastic samples that are completely independent. This new approach of stochastic sampling leads to a detector with significantly reduced complexity. It also allows reduction in the detector latency.