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      A Monte-Carlo Implementation of the SAGE Algorithm for Joint Soft Multiuser and Channel Parameter Estimation

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          Abstract

          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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          A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms

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            Space-alternating generalized expectation-maximization algorithm

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              Monte Carlo methods for signal processing: a review in the statistical signal processing context

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                Author and article information

                Journal
                2009-06-20
                Article
                10.1109/SPAWC.2009.5161876
                0906.3816
                9e06b47a-92ab-4423-9f05-3c867e1bd5c7

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                5 pages, 3 figures, 10th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2009
                cs.IT math.IT

                Numerical methods,Information systems & theory
                Numerical methods, Information systems & theory

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