Dispersive networks for nonlinear adaptive filters

The authors describe a dispersive network architecture that can be used for nonlinear adaptive channel equalization and signal prediction. Dispersive networks contain internal delay elements that spread out features in the input signal over time and space, so that they influence the output at multiple points in the future. When used for equalization, these networks can compensate for nonlinear channel distortions and achieve a lower error than conventional backpropagation networks of comparable size. In a signal prediction task, dispersive networks can adapt and predict simultaneously in an online environment, while conventional backpropagation networks require additional hardware.<>