On Data and Parameter Estimation Using the Variational Bayesian EMalgorithm for Blockfading Frequencyselective MIMO Channels 
Lars P. B. Christensen, Jan Larsen

Abstract  A general Variational Bayesian framework for iterative data and
parameter estimation for coherent detection is introduced as a
generalization of the EMalgorithm. Explicit solutions are given for
MIMO channel estimation with Gaussian prior and noise covariance
estimation with inverseWishart prior. Simulation of a GSMlike
system provides empirical proof that the VBEMalgorithm is able to
provide better performance than the EMalgorithm. However, if the
posterior distribution is highly peaked, the VBEMalgorithm
approaches the EMalgorithm 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. 
Type  Conference paper [With referee] 
Conference  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 
Year  2006 Month May 
Electronic version(s)  [pdf] 
BibTeX data  [bibtex] 
IMM Group(s)  Intelligent Signal Processing 