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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
지난 몇 년간 카오스를 이용한 의사소통을 위한 다양한 방안이 제안되어 왔다. 사용된 정확한 변조 방법에 관계없이 전송된 신호는 바람직하지 않게 신호에 왜곡을 도입하고 잡음을 추가하는 물리적 채널을 통과해야 합니다. 특히 코히어런트 기반 복조를 사용하는 경우 카오스 동기화에 필요한 프로세스를 실제로 구현하기가 어렵기 때문에 문제가 더욱 심각합니다. 본 논문에서는 카오스 기반 통신 시스템에서 채널 왜곡 문제를 해결하고 채널 등화 기법을 제안한다. 제안된 등화는 특정 훈련(등화) 알고리즘을 통합한 수정된 RNN(Recurrent Neural Network)에 의해 구현됩니다. 카오스 기반 통신 시스템에서 제안된 등화기의 성능을 입증하기 위해 컴퓨터 시뮬레이션이 사용되었습니다. Henon 맵과 Chua 회로는 혼란스러운 신호를 생성하는 데 사용됩니다. 제안된 RNN 기반 등화기는 기존 등화기보다 성능이 우수함을 보여줍니다.
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부
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
부
@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},}
부
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 -