Identification of Jamming Factors in Electronic Information System Based on Deep Learning

Li, Tingpeng; Wang, Manxi; Peng, Danhua; Yang, Xin‐She

doi:10.1109/icct.2018.8600065

Cited by 5 publications

(6 citation statements)

References 2 publications

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“…In the performance evaluation, the authors include multi-tone jammers. The method achieves 92% accuracy (which is similar to [290]), yet the authors remark that the method attains a higher learning speed. As major drawbacks, this study lacks comparison with a benchmark model and assumes an ideal jamming pattern, i.e., the jammer can instantaneously shift frequencies.…”

Section: B Anti-jamming Solutionsmentioning

confidence: 57%

See 1 more Smart Citation

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DLbased multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: B Anti-jamming Solutionsmentioning

confidence: 57%

“…Applying DL in this process can significantly overcome the inaccuracies of mathematical models. In [290], the authors proposed a CNN-based jammer identification method which imitates the image processing applications of DL. This method first converts one-dimensional data into image format in order to train the network.…”

Section: B Anti-jamming Solutionsmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

show abstract

“…In the performance evaluation, the authors include multi-tone jammers. The method achieves 92% accuracy (which is similar to [278]), yet the authors remark that the method attains a higher learning speed. As major drawbacks, this study lacks comparison with a benchmark model and assumes an ideal jamming pattern, i.e., the jammer can instantaneously shift frequencies.…”

Section: B Anti-jamming Solutionsmentioning

confidence: 57%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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“…Similarly, a fine tuning LeNet, with 1D sequences (size of 1*896) as inputs, also employed for 7 kinds of jammings identification in [569], which achieved an accuracy rate of 98%. In [570], a DL architecture was proposed to identify the jamming factors of electronic information system. The recognition method of four active jamming signal, based on CNN and STFT images as inputs, was proposed in [571], which achieved an accuracy rate of 99.86%, including blanket jamming, multiple false target jamming, narrow pulse jamming, and pure signal.…”

Section: A Jamming or Interference Classification And Recognitionmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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Identification of Jamming Factors in Electronic Information System Based on Deep Learning

Cited by 5 publications

References 2 publications

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

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