Enhanced chaotic communication with machine learning
Communication with chaotic signals holds a significant position in the field of secure communication and has consistently been research hotspot. While representative chaotic communication frameworks are all based on the deployment of robust synchronization or complex correlators, they pose considera...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2024-11-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0237990 |
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| _version_ | 1850137629464461312 |
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| author | Ji Xia Luonan Chen Huan-Fei Ma |
| author_facet | Ji Xia Luonan Chen Huan-Fei Ma |
| author_sort | Ji Xia |
| collection | DOAJ |
| description | Communication with chaotic signals holds a significant position in the field of secure communication and has consistently been research hotspot. While representative chaotic communication frameworks are all based on the deployment of robust synchronization or complex correlators, they pose considerable challenges to practical applications. In this work, a machine-learning-based framework is proposed for the chaotic shift keying scheme, which is robust against noise deterioration. Specifically, we adopt the reservoir computing technique with noise training schema to enhance the robustness of the entire communication process. Overall, the novel structure we propose fully leverages the predictive capabilities of neural networks, providing a new perspective for machine learning in the field of chaotic communication and significantly improving the accuracy of existing technologies. |
| format | Article |
| id | doaj-art-a57df139b0964b70b51ec24b83c5e9bb |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-a57df139b0964b70b51ec24b83c5e9bb2025-08-20T02:30:46ZengAIP Publishing LLCAIP Advances2158-32262024-11-011411115026115026-1610.1063/5.0237990Enhanced chaotic communication with machine learningJi Xia0Luonan Chen1Huan-Fei Ma2School of Mathematical Sciences, Soochow University, Suzhou 215001, People’s Republic of ChinaState Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, People’s Republic of ChinaSchool of Mathematical Sciences, Soochow University, Suzhou 215001, People’s Republic of ChinaCommunication with chaotic signals holds a significant position in the field of secure communication and has consistently been research hotspot. While representative chaotic communication frameworks are all based on the deployment of robust synchronization or complex correlators, they pose considerable challenges to practical applications. In this work, a machine-learning-based framework is proposed for the chaotic shift keying scheme, which is robust against noise deterioration. Specifically, we adopt the reservoir computing technique with noise training schema to enhance the robustness of the entire communication process. Overall, the novel structure we propose fully leverages the predictive capabilities of neural networks, providing a new perspective for machine learning in the field of chaotic communication and significantly improving the accuracy of existing technologies.http://dx.doi.org/10.1063/5.0237990 |
| spellingShingle | Ji Xia Luonan Chen Huan-Fei Ma Enhanced chaotic communication with machine learning AIP Advances |
| title | Enhanced chaotic communication with machine learning |
| title_full | Enhanced chaotic communication with machine learning |
| title_fullStr | Enhanced chaotic communication with machine learning |
| title_full_unstemmed | Enhanced chaotic communication with machine learning |
| title_short | Enhanced chaotic communication with machine learning |
| title_sort | enhanced chaotic communication with machine learning |
| url | http://dx.doi.org/10.1063/5.0237990 |
| work_keys_str_mv | AT jixia enhancedchaoticcommunicationwithmachinelearning AT luonanchen enhancedchaoticcommunicationwithmachinelearning AT huanfeima enhancedchaoticcommunicationwithmachinelearning |