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

Advanced Markov Chain Monte Carlo Methods for Iterative (Turbo) Multiuser Detection
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用于迭代（涡轮）多用户检测的高级马尔可夫链蒙特卡罗方法

DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Jürgen Lindner
Jürgen Lindner
中科院分区：
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文献类型：
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作者：
M. Dangl;Zhenning Shi;M. Reed;Jürgen Lindner

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近年来，马尔可夫链蒙特卡罗(MCMC)采样方法已发展成为解决多用户和多输入多输出(MIMO)检测问题的新方法。基于Gibbs抽样的方法作为MCMC方法的一种特殊类型，由于其在性能和复杂度之间的良好折衷而得到很好的适合。然而，众所周知，基于Gibbs采样的检测方法在高信噪比(SNR)条件下可能会表现出性能下降。我们提出了一种改进的软输入软输出算法，这种退化效应得到了很大程度的缓解。在过载的码分多址(CDMA)系统中使用该算法进行Turbo多用户检测，与其他已知的检测方案相比，获得了优异的性能，同时需要中等的计算复杂度。
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.