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Minimal data set.

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Titel: Minimal data set.
Autoren: Hui Zhao, Guobin Zhao, Xichun Wang, Zhonghui Zhang, Xianchao Xun
Publikationsjahr: 2025
Schlagwörter: 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, %22">xlink ">
Beschreibung: 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.
Publikationsart: article in journal/newspaper
Sprache: unknown
Relation: https://figshare.com/articles/journal_contribution/Minimal_data_set_/28857638
DOI: 10.1371/journal.pone.0319953.s001
Verfügbarkeit: https://doi.org/10.1371/journal.pone.0319953.s001
https://figshare.com/articles/journal_contribution/Minimal_data_set_/28857638
Rights: CC BY 4.0
Dokumentencode: edsbas.682C8FCF
Datenbank: BASE
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  Data: Minimal data set.
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  Data: <searchLink fieldCode="AR" term="%22Hui+Zhao%22">Hui Zhao</searchLink><br /><searchLink fieldCode="AR" term="%22Guobin+Zhao%22">Guobin Zhao</searchLink><br /><searchLink fieldCode="AR" term="%22Xichun+Wang%22">Xichun Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhonghui+Zhang%22">Zhonghui Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Xianchao+Xun%22">Xianchao Xun</searchLink>
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Space+Science%22">Space Science</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+Sciences+not+elsewhere+classified%22">Biological Sciences not elsewhere classified</searchLink><br /><searchLink fieldCode="DE" term="%22wireless+communication+technology%22">wireless communication technology</searchLink><br /><searchLink fieldCode="DE" term="%22target+communication+systems%22">target communication systems</searchLink><br /><searchLink fieldCode="DE" term="%22static+spectrum+allocation%22">static spectrum allocation</searchLink><br /><searchLink fieldCode="DE" term="%22reduce+error+rates%22">reduce error rates</searchLink><br /><searchLink fieldCode="DE" term="%22processing+large+amounts%22">processing large amounts</searchLink><br /><searchLink fieldCode="DE" term="%22key+factor+affecting%22">key factor affecting</searchLink><br /><searchLink fieldCode="DE" term="%22jamming+communication+technology%22">jamming communication technology</searchLink><br /><searchLink fieldCode="DE" term="%22electromagnetic+spectrum+environment%22">electromagnetic spectrum environment</searchLink><br /><searchLink fieldCode="DE" term="%22also+precisely+defines%22">also precisely defines</searchLink><br /><searchLink fieldCode="DE" term="%22interference+communication+technology%22">interference communication technology</searchLink><br /><searchLink fieldCode="DE" term="%22identifying+interference+signals%22">identifying interference signals</searchLink><br /><searchLink fieldCode="DE" term="%22generates+interference+signals%22">generates interference signals</searchLink><br /><searchLink fieldCode="DE" term="%22fixed+interference+patterns%22">fixed interference patterns</searchLink><br /><searchLink fieldCode="DE" term="%22providing+new+means%22">providing new means</searchLink><br /><searchLink fieldCode="DE" term="%22proposed+model+achieves%22">proposed model achieves</searchLink><br /><searchLink fieldCode="DE" term="%22processing+signal+features%22">processing signal features</searchLink><br /><searchLink fieldCode="DE" term="%22model+effectively+addresses%22">model effectively addresses</searchLink><br /><searchLink fieldCode="DE" term="%22deep+neural+networks%22">deep neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22end+strategy+optimization%22">end strategy optimization</searchLink><br /><searchLink fieldCode="DE" term="%22interference+accuracy+rate%22">interference accuracy rate</searchLink><br /><searchLink fieldCode="DE" term="%22new+model%22">new model</searchLink><br /><searchLink fieldCode="DE" term="%22end+optimization%22">end optimization</searchLink><br /><searchLink fieldCode="DE" term="%22accuracy+rate%22">accuracy rate</searchLink><br /><searchLink fieldCode="DE" term="%22interference+algorithm%22">interference algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22signal+transmission%22">signal transmission</searchLink><br /><searchLink fieldCode="DE" term="%22model+based%22">model based</searchLink><br /><searchLink fieldCode="DE" term="%22deep+q%22">deep q</searchLink><br /><searchLink fieldCode="DE" term="%22xlink+">%22">xlink "></searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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.
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  Data: https://figshare.com/articles/journal_contribution/Minimal_data_set_/28857638
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1371/journal.pone.0319953.s001
– Name: URL
  Label: Availability
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  Data: https://doi.org/10.1371/journal.pone.0319953.s001<br />https://figshare.com/articles/journal_contribution/Minimal_data_set_/28857638
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        Value: 10.1371/journal.pone.0319953.s001
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      – SubjectFull: Space Science
        Type: general
      – SubjectFull: Biological Sciences not elsewhere classified
        Type: general
      – SubjectFull: wireless communication technology
        Type: general
      – SubjectFull: target communication systems
        Type: general
      – SubjectFull: static spectrum allocation
        Type: general
      – SubjectFull: reduce error rates
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      – SubjectFull: processing large amounts
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      – SubjectFull: key factor affecting
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      – SubjectFull: jamming communication technology
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      – SubjectFull: electromagnetic spectrum environment
        Type: general
      – SubjectFull: also precisely defines
        Type: general
      – SubjectFull: interference communication technology
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      – SubjectFull: identifying interference signals
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      – SubjectFull: generates interference signals
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      – SubjectFull: fixed interference patterns
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      – SubjectFull: end optimization
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      – TitleFull: Minimal data set.
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            NameFull: Hui Zhao
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              Type: published
              Y: 2025
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