Variational Bayesian blind estimation of simo channels
Koji Harada, Hideaki Sakai · 2010
We propose an application of variational Bayesian inference to the problem of blind estimation of single-input multiple-output (SIMO), finite-impulse-response (FIR) channels. The blind channel estimation problem has been extensively studied well over a decade. Nevertheless, there are some types of channel that cannot be successfully estimated by existing blind methods. One example of such a channel type is frequency-selective, exponentially-weighted Rayleigh fading channel. Our blind estimation approach to it is to incorporate prior knowledge on channel weighting in the form of channel prior distribution, then obtain the approximation to the posterior distribution of the channel using the variational Bayesian framework. Despite the error due to the mean field approximation involved in the variational framework, the derived algorithm has been verified by simulation to provide much improvements over the existing ones in a statistical sense.