Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31973
Title: Channel equalization for chaos-based communication systems
Authors: Feng, JC
Tse, CK 
Lau, FCM 
Keywords: Channel equalization
Chaos-based communications
Recurrent neural networks
Tracking of chaotic signals
Issue Date: 2002
Source: IEICE Transactions on fundamentals of electronics, communications and computer sciences, 2002, v. E85-A, no. 9, p. 2015-2024 How to cite?
Journal: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 
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.
URI: http://hdl.handle.net/10397/31973
ISSN: 0916-8508
Appears in Collections:Conference Paper

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