A Variational Bayes Approach to Blind Channel Estimation
Koji Harada, Hideaki Sakai · Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications · 2010
Blind estimation of single-input multiple-output (SIMO) finite-impulse-response (FIR) channels has been extensively studied over the recent years. However, due to the difficult nature of the blind problem, there are certain classes of channels that cannot be successfully estimated by existing blind algorithms. Such examples include FIR channels with trailing (or preceding) close-to-zero coefficients, driven by correlated input signal. In this case, classical methods based on the second-order statistics fail to provide accurate blind channel estimates. Moreover, if the channel is not minimum phase, the estimation task is more complicated. In this contribution, we explore an alternative approach utilizing hierarchical Bayesian model together with variational approximation. With this approach, blind estimation error for the difficult channels can be significantly improved, which was verified via numerical simulation.