Quasi-Newton filtered-error and filtered-regressor algorithms for adaptive equalization and deconvolution

In equalization and deconvolution tasks, the correlated nature of the input signal slows the convergence speeds of stochastic gradient adaptive filters. In this paper, we present two simple algorithms that employ the equalizer as a prewhitening filter to effectively and iteratively decorrelate the input signal within the gradient updates. These algorithms provide quasi-Newton convergence locally about the optimum coefficient solution for deconvolution and equalization tasks. Simulations indicate that the algorithms have excellent adaptation properties both for supervised and unsupervised (blind) adaptation criteria.