Neuro-computational frameworks for non-linear stochastic wireless channels

Abstract

Inter symbol interference and related distortions observed in high data rate wireless systems are mitigated using equalizers and channel estimation methods which are traditionally based on data driven tapped delay line (TDL) models. With increased fading due to mobility of transmit receive terminals, data driven schemes prove to be inefficient. Further, the TDL model assumes the dispersive channel to be linear in nature unlike the practical case which is non-linear, resulting in degraded performance. In such a situation, extrapolation of the channel state information is required which has been done by using artificial neural networks (ANN) optimized for non-linear channels. We explore a few neuro-computational approaches namely focused time delay neural network, feed forward ANN (FANN)–decision feedback equalizer (DFE) (FANN–DFE) and a functional link neural network while modeling non-linear wireless channels. Further, implementation of FANN–DFE based algorithm has been carried out using a specialized hardware framework to show the reduction of computational latency during training to establish its suitability for real time applications.