A Reduced-State-Space Markov Chain Monte Carlo Method for Iterative Spatial Multiplexing MIMO

Markov Chain Monte Carlo (MCMC) method applied as Multiple-Input-Multiple-Output (MIMO) detector has shown near capacity performance. However, the conventional MCMC method suffers from an error floor in the high signal-to-noise (SNR) region. This paper proposes a novel robust reduced-state-space MCMC (RSS-MCMC) method, which utilizes the a priori information for the first time to qualify the reliable decoded bits from the entire signal space. The new robust MCMC method is developed to deal with the unreliable bits by using the reliably decoded bit information to cancel the interference that they generate. The performance comparison shows that the new technique has improved performance compared to the conventional approach, and further complexity reduction can be obtained with the assistance of the a priori information. Furthermore, the complexity and performance tradeoff of the new method can be optimized for practical realizations.