Advanced Markov Chain Monte Carlo Methods for Iterative (Turbo) Multiuser Detection
M.A. Dangl, Zhenning Shi, Mark C. Reed, Juergen Lindner · 2006
Recently, Markov Chain Monte Carlo (MCMC) sampling methods have evolved as new promising solutions to both multiuser and multiple-input multiple-output (MIMO) detection problems. Approaches based on Gibbs sampling as a special type of MCMC methods are well suited due to their good trade-off between performance and complexity. However, it is known that detection methods based on Gibbs sampling show a performance degradation in the high signal-to-noise ratio (SNR) regime. We propose an improved version of a soft-input soft-output algorithm, where this degradation effect is considerably mitigated. Employing the algorithm for turbo multiuser detection in overloaded code-division multiple-access (CDMA) systems yields excellent performance in comparison to other known detection schemes while offering moderate computational complexity. 1