Neural Networks-Based Turbo Equalization of a Satellite Communication Channel

Abstract

This paper proposes neural networks-based turbo equalization (TEQ) applied to a non linear channel. Based on a Volterra model of the satellite non linear communication channel, we derive a soft input soft output (SISO) radial basis function (RBF) equalizer that can be used in an iterative equalization in order to improve the system performance. In particular, it is shown that the RBF-based TEQ is able to achieve its matched filter bound (MFB) within few iterations. The paper also proposes a blind implementation of the TEQ using a multilayer perceptron (MLP) as an adaptive model of the nonlinear channel. Asymptotic analysis as well as reduced complexity implementations are also presented and discussed.