Modified back-propagation algorithm applied to decision-feedback equalisation

Abstract

This study presents a modified back-propagation (BP) algorithm for a multilayer perceptron (MLP) to perfect its ability to cope with the problem of binary phase shift keying channel equalisation. For a typical BP algorithm, the error signal is obtained from the comparison between the target and estimated signal. The error signal is propagated layer by layer from the output layer to the input layer to adaptively adjust all weights in the MLP. Therefore all parameters of the MLP are obtained by a single BP algorithm. However, the structure of the MLP with a hidden layer provides the feasibility to modify the BP algorithm to improve its performance. The MLP can be divided from the hidden layer into two sub-MLPs, and each sub-MLP is optimised by its own BP algorithm. Accordingly, the whole MLP is adjusted by two BP algorithms independently. In this study, the modified BP algorithm is utilised to cope with the problem of channel equalisation. The simulation results show that the modified BP algorithm indeed improves the typical BP algorithm especially for an environment with nonlinear distortion, frequency offset, and phase and timing errors. Moreover, the computation complexity of the proposed algorithm almost equals that of the conventional BP algorithm.