The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Vários esquemas foram propostos para comunicação usando o caos nos últimos anos. Independentemente do método de modulação exacto utilizado, o sinal transmitido deve passar através de um canal físico que introduz indesejavelmente distorção no sinal e adiciona-lhe ruído. O problema é particularmente sério quando a demodulação baseada em coerência é usada porque o processo necessário de sincronização do caos é difícil de implementar na prática. Este artigo aborda o problema de distorção de canal e propõe uma técnica para equalização de canais em sistemas de comunicação baseados em caos. A equalização proposta é realizada por uma rede neural recorrente modificada (RNN) que incorpora um algoritmo de treinamento (equalização) específico. Simulações computacionais são utilizadas para demonstrar o desempenho do equalizador proposto em sistemas de comunicação baseados no caos. O mapa de Henon e o circuito de Chua são usados para gerar sinais caóticos. É mostrado que o equalizador baseado em RNN proposto supera os equalizadores convencionais.
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Jiu-chao FENG, Chi Kong TSE, Francis C. M. LAU, "Channel Equalization for Chaos-Based Communication Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 9, pp. 2015-2024, September 2002, doi: .
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_9_2015/_p
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@ARTICLE{e85-a_9_2015,
author={Jiu-chao FENG, Chi Kong TSE, Francis C. M. LAU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Channel Equalization for Chaos-Based Communication Systems},
year={2002},
volume={E85-A},
number={9},
pages={2015-2024},
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.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Channel Equalization for Chaos-Based Communication Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2015
EP - 2024
AU - Jiu-chao FENG
AU - Chi Kong TSE
AU - Francis C. M. LAU
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2002
AB - 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.
ER -