Paper Info
Title
Reconstruction Of Chaotic Signals With Application To Channel Equalization In Chaos-Based 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 Henon 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 (C) 2004 John Wiley Sons, Ltd.
Year
DOI
Venue
2004
10.1002/dac.639
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
Keywords
DocType
Volume
chaos, communications, recurrent neural networks, channel equalization
Journal
17
Issue
ISSN
Citations 
3
1074-5351
3
PageRank 
References 
Authors
0.49
9
3
Name
Order
Citations
PageRank
Jiuchao Feng113317.84
Chi Kong Tse224540.24
Francis C. M. Lau31942181.31