Self-whitening 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. Prewhitening techniques have been proposed to improve convergence performance, but the additional coefficient memory and updates for the prewhitening filter can be prohibitive in some applications. In this correspondence, we present two simple algorithms that employ the equalizer as a prewhitening filter within the gradient updates. These self-whitening algorithms provide quasi-Newton convergence locally about the optimum coefficient solution for deconvolution and equalization tasks. Multichannel extensions of the techniques are also described.