Bayesian blind equalization of time-varying frequency-selective channels subject to unknown variance noise

We present in this article a novel particle-filter-based blind equalization algorithm suitable for FIR time-varying frequency-selective communication channels corrupted by unknown variance additive Gaussian noise. The proposed method is fully Bayesian, integrating out the unknown parameters via an original recursive method, unlike previous approaches that rely on suboptimal plug-in estimates. We verify via numerical simulations that the proposed method's performance approaches that of the trained MAP equalizer, exceeding that of the linear least squares Kalman equalizer for medium to low noise levels.