Reconstruction of chaotic signals with application to channel equalization in chaos-based communication systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)217-232
Journal / PublicationInternational Journal of Communication Systems
Volume17
Issue number3
Online published17 Mar 2004
Publication statusPublished - Apr 2004
Externally publishedYes

Abstract

A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent-based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos-based communication systems. The Hénon map and Chua's circuit are used to generate chaotic signals. It is shown that the proposed RNN-based equalizer outperforms conventional equalizers as well as those based on feedforward neural networks for noisy, distorted linear and non-linear channels. Copyright © 2004 John Wiley & Sons, Ltd.

Research Area(s)

  • Channel equalization, Chaos, Communications, Recurrent neural networks