Robust blind joint data/channel estimation based on bilinear optimization

In the context of digital radio communications, the signals are transmitted through propagation channels which introduce intersymbol interference (ISI). The channels can be represented as FIR filters which have to be identified and/or equalized for the transmitted symbols to be recovered. The problem of identifying/equalizing a digital communication channel based on its temporally or spatially oversampled output has gained much attention (single-input/multiple-output-SIMO-deconvolution). In this context, we propose a new joint data/channel estimation method. Our technique relies on the minimization of a bilinear MSE cost function, where the variables to be adjusted are the channel coefficient matrix and a linear equalizer. We show that this a priori choice of a linear equalization structure allows the derivation of a second-order unimodal criterion, leading to globally convergent identification/equalization schemes. The proposed method is completely blind in that (1) no assumption is required upon the transmitted sequence statistics or alphabet, and (2) it shows some robustness with respect to the channel order estimation problem (thus improving on most previous related works). It also allows the free choice of a delay in the equalizer so that output noise amplification can be optimized.