Noise Robust Adaptive Blind Channel Identification Using Spectral Constraints

A class of adaptive blind channel identification algorithms were proposed recently and were demonstrated to be able to successfully identify various types of channels when the observed signals are free from significant levels of measurement noise. In this paper, we provide a study of the effects of noise on these algorithms and show that they misconverge even at moderate values of SNR. We introduce a spectral constraint into the adaptation rule and show that the robustness to noise can be considerably improved. Simulation results are presented for the new algorithm, which demonstrate a significant performance improvement in terms of normalized projection misalignment