2023
DOI: 10.3390/electronics12163425
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A CNN-Based Adaptive Federated Learning Approach for Communication Jamming Recognition

Abstract: The effective and accurate recognition of communication jamming is crucial for enhancing the anti-jamming capability of wireless communication systems. At present, a significant portion of jamming data is decentralized, stored in local nodes, and cannot be uploaded directly for network training due to its sensitive nature. To address this challenge, we introduce a novel distributed jamming recognition method. This method leverages a distributed recognition framework to achieve global optimization through feder… Show more

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Cited by 2 publications
(1 citation statement)
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“…Machine learning algorithms are also trained using spectrogram images [30], IQ samples [31], time-domain signal samples [32], and FFT samples [33,34] for jamming detection. Although machine learning algorithms are becoming increasingly popular, the issues of training these algorithms, collecting sufficient data for training, adapting to varying jamming strategies, and integrating them into the system architecture with minimal overhead must be considered.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning algorithms are also trained using spectrogram images [30], IQ samples [31], time-domain signal samples [32], and FFT samples [33,34] for jamming detection. Although machine learning algorithms are becoming increasingly popular, the issues of training these algorithms, collecting sufficient data for training, adapting to varying jamming strategies, and integrating them into the system architecture with minimal overhead must be considered.…”
Section: Introductionmentioning
confidence: 99%