PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On Data and Parameter Estimation Using the Variational Bayesian EM-algorithm for Block-fading Frequency-selective MIMO Channels
Lars Christensen and Jan Larsen
Proceesings of ICASSP Volume IV, pp. 465-468, 2006. ISSN 1520-6149


A general Variational Bayesian framework for iterative data and parameter estimation for coherent detection is introduced as a generalization of the EM-algorithm. Explicit solutions are given for MIMO channel estimation with Gaussian prior and noise covariance estimation with inverse-Wishart prior. Simulation of a GSM-like system provides empirical proof that the VBEM-algorithm is able to provide better performance than the EM-algorithm. However, if the posterior distribution is highly peaked, the VBEM-algorithm approaches the EM-algorithm and the gain disappears. The potential gain is therefore greatest in systems with a small amount of observations compared to the number of parameters to be estimated.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2897
Deposited By:Jan Larsen
Deposited On:23 November 2006