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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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              Propagation delay estimation in asynchronous direct-sequence code-division multiple access systems

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                Journal
                0906.3816

                Numerical methods, Information systems & theory

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