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.
Original language | English |
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Pages (from-to) | 217-232 |
Number of pages | 16 |
Journal | International Journal of Communication Systems |
Volume | 17 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Apr 2004 |
Keywords
- Channel equalization
- Chaos
- Communications
- Recurrent neural networks
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Networks and Communications