Advanced Markov Chain Monte Carlo Methods for Iterative (Turbo) Multiuser Detection

摘要

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 may 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 requiring moderate computational complexity.