Fast Fading Channel Neural Equalization Using Levenberg-Marquardt Training Algorithm and Pulse Shaping Filters

Tiago Mota, Jorgean Leal, Antônio Lima

Departament of Electrical Engineering, Federal University of Bahia, Bahia, Brazil.
Departament of Mechatronics, Federal University of Bahia, Bahia, Brazil.
National Telecommunications Agency of Brazil, Bahia, Brazil.

DOI: 10.4236/ijcns.2014.72008                

Abstract

Artificial Neural Network (ANN) equalizers have been successfully applied to mitigate Inter symbolic Interference (ISI) due to distortions introduced by linear or nonlinear communication channels. The ANN architecture is chosen according to the type of ISI produced by fixed, fast or slow fading channels. In this work, we propose a combination of two techniques in order to minimize ISI yield by fast fading channels, i.e., pulse shape filtering and ANN equalizer. Levenberg-Marquardt algorithm is used to update the synaptic weights of an ANN comprise only by two recurrent perceptrons. The proposed system outperformed more complex structures such as those based on Kalman filtering approach.

Keywords

Decision Feedback Equalizers; Levenberg-Marquardt Algorithm; Pulse Shaping; Recurrent Neural Networks

Conflicts of Interest

The authors declare no conflicts of interest.

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