The classification method of the alliance game.
<div><p>Against the backdrop of the rapid development of wireless communication technology, the complex signal interference issues in the electromagnetic spectrum environment have become a key factor affecting the quality and reliability of signal transmission. Existing solutions, such a...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852021083458240512 |
|---|---|
| author | Hui Zhao (7395) |
| author2 | Guobin Zhao (16641096) Xichun Wang (8988644) Zhonghui Zhang (199151) Xianchao Xun (21175163) |
| author2_role | author author author author |
| author_facet | Hui Zhao (7395) Guobin Zhao (16641096) Xichun Wang (8988644) Zhonghui Zhang (199151) Xianchao Xun (21175163) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hui Zhao (7395) Guobin Zhao (16641096) Xichun Wang (8988644) Zhonghui Zhang (199151) Xianchao Xun (21175163) |
| dc.date.none.fl_str_mv | 2025-04-24T17:28:43Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0319953.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_classification_method_of_the_alliance_game_/28857644 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Biological Sciences not elsewhere classified wireless communication technology target communication systems static spectrum allocation reduce error rates processing large amounts key factor affecting jamming communication technology electromagnetic spectrum environment also precisely defines interference communication technology identifying interference signals generates interference signals fixed interference patterns providing new means proposed model achieves processing signal features model effectively addresses deep neural networks end strategy optimization interference accuracy rate new model end optimization accuracy rate interference algorithm signal transmission model based deep q xlink "> used metrics time data test conditions network models longer able improve adaptability game theory feature parameters experiments show existing solutions dynamic environments |
| dc.title.none.fl_str_mv | The classification method of the alliance game. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Against the backdrop of the rapid development of wireless communication technology, the complex signal interference issues in the electromagnetic spectrum environment have become a key factor affecting the quality and reliability of signal transmission. Existing solutions, such as traditional interference suppression techniques that rely on static spectrum allocation and fixed interference patterns, are no longer able to adapt to the rapidly changing electromagnetic environment and face computational complexity challenges when processing large amounts of real-time data. This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. By extracting and processing signal features through deep neural networks, and dynamically adjusting communication strategies with near-end optimization, the model effectively addresses the recognition and prediction of signal transmission feature parameters in target communication systems, generates interference signals with the same feature parameters, and achieves effective interference suppression. Experiments show that the proposed model achieves an accuracy rate of 95.23% in identifying interference signals and an anti-interference accuracy rate of 85.47%, significantly outperforming random forest and deep Q-network models. The study not only clarifies the limitations of existing solutions but also precisely defines the goals of the new model, which are to reduce error rates and improve adaptability in dynamic environments. The results further explain the significance of the used metrics and test conditions, providing new means and strategies for the development of anti-interference communication technology, especially in dealing with new complex electromagnetic spectrum interference.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_2861a9abda1dfec8a33937bc8d5fbf45 |
| identifier_str_mv | 10.1371/journal.pone.0319953.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28857644 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The classification method of the alliance game.Hui Zhao (7395)Guobin Zhao (16641096)Xichun Wang (8988644)Zhonghui Zhang (199151)Xianchao Xun (21175163)Space ScienceBiological Sciences not elsewhere classifiedwireless communication technologytarget communication systemsstatic spectrum allocationreduce error ratesprocessing large amountskey factor affectingjamming communication technologyelectromagnetic spectrum environmentalso precisely definesinterference communication technologyidentifying interference signalsgenerates interference signalsfixed interference patternsproviding new meansproposed model achievesprocessing signal featuresmodel effectively addressesdeep neural networksend strategy optimizationinterference accuracy ratenew modelend optimizationaccuracy rateinterference algorithmsignal transmissionmodel baseddeep qxlink ">used metricstime datatest conditionsnetwork modelslonger ableimprove adaptabilitygame theoryfeature parametersexperiments showexisting solutionsdynamic environments<div><p>Against the backdrop of the rapid development of wireless communication technology, the complex signal interference issues in the electromagnetic spectrum environment have become a key factor affecting the quality and reliability of signal transmission. Existing solutions, such as traditional interference suppression techniques that rely on static spectrum allocation and fixed interference patterns, are no longer able to adapt to the rapidly changing electromagnetic environment and face computational complexity challenges when processing large amounts of real-time data. This study proposes an intelligent anti-interference algorithm that combines deep neural networks and game theory, and constructs a model based on near-end strategy optimization. By extracting and processing signal features through deep neural networks, and dynamically adjusting communication strategies with near-end optimization, the model effectively addresses the recognition and prediction of signal transmission feature parameters in target communication systems, generates interference signals with the same feature parameters, and achieves effective interference suppression. Experiments show that the proposed model achieves an accuracy rate of 95.23% in identifying interference signals and an anti-interference accuracy rate of 85.47%, significantly outperforming random forest and deep Q-network models. The study not only clarifies the limitations of existing solutions but also precisely defines the goals of the new model, which are to reduce error rates and improve adaptability in dynamic environments. The results further explain the significance of the used metrics and test conditions, providing new means and strategies for the development of anti-interference communication technology, especially in dealing with new complex electromagnetic spectrum interference.</p></div>2025-04-24T17:28:43ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0319953.g002https://figshare.com/articles/figure/The_classification_method_of_the_alliance_game_/28857644CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288576442025-04-24T17:28:43Z |
| spellingShingle | The classification method of the alliance game. Hui Zhao (7395) Space Science Biological Sciences not elsewhere classified wireless communication technology target communication systems static spectrum allocation reduce error rates processing large amounts key factor affecting jamming communication technology electromagnetic spectrum environment also precisely defines interference communication technology identifying interference signals generates interference signals fixed interference patterns providing new means proposed model achieves processing signal features model effectively addresses deep neural networks end strategy optimization interference accuracy rate new model end optimization accuracy rate interference algorithm signal transmission model based deep q xlink "> used metrics time data test conditions network models longer able improve adaptability game theory feature parameters experiments show existing solutions dynamic environments |
| status_str | publishedVersion |
| title | The classification method of the alliance game. |
| title_full | The classification method of the alliance game. |
| title_fullStr | The classification method of the alliance game. |
| title_full_unstemmed | The classification method of the alliance game. |
| title_short | The classification method of the alliance game. |
| title_sort | The classification method of the alliance game. |
| topic | Space Science Biological Sciences not elsewhere classified wireless communication technology target communication systems static spectrum allocation reduce error rates processing large amounts key factor affecting jamming communication technology electromagnetic spectrum environment also precisely defines interference communication technology identifying interference signals generates interference signals fixed interference patterns providing new means proposed model achieves processing signal features model effectively addresses deep neural networks end strategy optimization interference accuracy rate new model end optimization accuracy rate interference algorithm signal transmission model based deep q xlink "> used metrics time data test conditions network models longer able improve adaptability game theory feature parameters experiments show existing solutions dynamic environments |