联合软多用户和信道参数估计的SAGE算法的Monte-Carlo实现
A Monte-Carlo Implementation of the SAGE Algorithm for Joint Soft Multiuser and Channel Parameter Estimation
摘要：
基于空间交替广义期望最大化（SAGE）框架，提出了一种适用于异步直接序列码分多址（DS-CDMA）系统上行链路的传输时延和信道参数联合估计算法。未知参数的边际似然，平均的数据序列，以及SAGE算法的期望和最大化的步骤推导出解析。为了实现所提出的算法，马尔可夫链蒙特卡罗（MCMC）技术，称为吉布斯采样，采用计算的{ \em后验}概率的数据符号的计算效率的方式。计算机模拟表明，该算法具有良好的估计性能。这种所谓的MCMCMC-SAGE接收机保证在似然性上收敛。
英文摘要：
An efficient, joint transmission delay and channel parameter estimation algorithm is proposed for uplink asynchronous direct-sequence code-division multiple access (DS-CDMA) systems based on the space-alternating generalized expectation maximization (SAGE) framework. The marginal likelihood of the unknown parameters, averaged over the data sequence, as well as the expectation and maximization steps of the SAGE algorithm are derived analytically. To implement the proposed algorithm, a Markov Chain Monte Carlo (MCMC) technique, called Gibbs sampling, is employed to compute the {\em a posteriori} probabilities of data symbols in a computationally efficient way. Computer simulations show that the proposed algorithm has excellent estimation performance. This so-called MCMC-SAGE receiver is guaranteed to converge in likelihood.
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