Advanced Markov Chain Monte Carlo Methods for Iterative (Turbo) Multiuser Detection
Conference: TURBO - CODING - 2006 - 4th International Symposium on Turbo Codes & Related Topics; 6th International ITG-Conference on Source and Channel Coding
04/03/2006 - 04/07/2006 at Munich, Germany
Proceedings: TURBO - CODING - 2006
Pages: 6Language: englishTyp: PDF
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Authors:
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)
Abstract:
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.