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

Konferenz: TURBO - CODING - 2006 - 4th International Symposium on Turbo Codes & Related Topics; 6th International ITG-Conference on Source and Channel Coding
03.04.2006 - 07.04.2006 in Munich, Germany

Tagungsband: TURBO - CODING - 2006

Seiten: 6Sprache: EnglischTyp: PDF

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Dangl, Markus A.; Lindner, Jürgen (University of Ulm, Dept. of Information Technology, Albert-Einstein-Allee 43, 89081 Ulm, Germany)
Shi, Zhenning; Reed, Mark C. (National ICT Australia and Australian National University, Locked Bag 8001, Canberra ACT 2601, Australia)

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.