Split FTDNN-DFE Equalizer for Complex Non-linear Wireless Channels with NARMA Approximation

Abstract

AbstractTime-dependent channel variations in wireless systems have been modeled using autoregressive (AR), moving average, and AR moving average (ARMA) approaches, which in certain conditions fail to provide the most suitable description of the scenarios. It lowers the quality of service, increases the dependence on the traditional tapped delay line model, and requires more reference symbols as fading in the channel increases. Non-linear attributes in channels are best described by non-linear AR (NAR) and non-linear ARMA (NARMA) models, which capture the channel state information (CSI) better. Learning-based systems like artificial neural networks (ANN) are better equipped to deal with NAR and NARMA approximations of channels, recover CSI, and provide proper channel estimation due to their natural association with non-linear computing. Though decision feedback equalizers (DFE) are the traditional and preferred approaches to reduce the ill effects of channel-induced variations, these are unable to mitigate noise-triggered responses that ANNs can. Here, DFEs and special forms of ANNs are combined to recover CSI from NAR and NARMA approximations of time-varying channels. We especially configure a class of fully focused time delay neural network designed in split form in unitary and composite modes with a DFE to track real and imaginary components of signals. Experimental results establish the effectiveness of such hybrid tools in tracking time-varying non-linear channels covering International Telecommunication Union pedestrian to vehicular conditions.