Non-linear equalization on communication channels.

Author(s)

Magno Teófilo Madeira da Silva

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2001-04-25

Examining board members

Max Gerken; Dalton Soares Arantes; Phillip Mark Seymou Burt

• Advisor: Max Gerken

Abstract

Equalization of communication channels using neural networks is investigated by considering three kinds of networks: MLP (Multilayer Perceptron), RBF (Radial Basis Function) and RNN (Recurrent Neural Network). The performance of the nonlinear equalizers based on these networks are compared with the linear transversal equalizer and the optimal equalizers given by the bayesian and maximum likelihood criteria. Binary and quaternary alphabets are used and transmitted over finite pulse response channel models. Decision feedback is considered whenever it is worthwhile. The training of these equalizers is considered in the supervised form and a comparison of some training algorithms has been performed. In this scope, a new algorithm based on parameter acceleration is introduced for the training of MLP networks. Moreover, a hybrid equalizer composed of a linear transversal equalizer and a RNN network is proposed. It is a simple and flexible nonlinear structure making use of decision feedback. imulation results show that it may be advantageously used to equalize linear and nonlinear channels. (AU)