Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9797
Title: Reconstruction of chaotic signals with application to channel equalization in chaos-based communication systems
Authors: Feng, J
Tse, CK 
Lau, FCM 
Keywords: Channel equalization
Chaos
Communications
Recurrent neural networks
Issue Date: 2004
Source: International journal of communication systems, 2004, v. 17, no. 3, p. 217-232 How to cite?
Journal: International Journal of Communication Systems 
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.
URI: http://hdl.handle.net/10397/9797
ISSN: 1074-5351
DOI: 10.1002/dac.639
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