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Reconstruction of chaotic signals with application to channel equalization in chaos‐based communication systems
Author(s) -
Feng Jiuchao,
Tse Chi K.,
Lau Francis C. M.
Publication year - 2004
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.639
Subject(s) - computer science , demodulation , blind equalization , channel (broadcasting) , chaotic , equalization (audio) , chaos (operating system) , distortion (music) , synchronization (alternating current) , noise (video) , signal (programming language) , communications system , decoding methods , feed forward , control theory (sociology) , algorithm , electronic engineering , telecommunications , artificial intelligence , bandwidth (computing) , amplifier , computer security , control (management) , control engineering , engineering , image (mathematics) , programming language
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.