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
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.