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Distributed Few-Shot Learning for Intelligent Recognition of Communication Jamming

Liu, Mingqian and Liu, Zilong and Lu, Weidang and Chen, Yunfei and Gao, Xiaoteng and Zhao, Nan (2022) 'Distributed Few-Shot Learning for Intelligent Recognition of Communication Jamming.' IEEE Journal of Selected Topics in Signal Processing, 16 (3). pp. 395-405. ISSN 1932-4553

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Effective recognition of communication jamming is of vital importance in improving wireless communication system’s anti-jamming capability. Motivated by the major challenges that the jamming data sets in wireless communication system are often small and the recognition performance may be poor, we introduce a novel jamming recognition method based on distributed few-shot learning in this paper. Our proposed method employs a distributed recognition architecture to achieve the global optimization of multiple sub-networks by federated learning. It also introduces a dense block structure in the sub-network structure to improve network information flow by the feature multiplexing and configuration bypass to improve resistance to over-fitting. Our key idea is to first obtain the time-frequency diagram, fractional Fourier transform and constellation diagram of the communication jamming signal as the model-agnostic meta-learning network input, and then train the distributed network through federated learning for jamming recognition. Simulation results show that our proposed method leads to excellent recognition performance with a small data set.

Item Type: Article
Uncontrolled Keywords: Jamming; Frequency modulation; Time-frequency analysis; Feature extraction; Wireless communication; Collaborative work; Training; Federated learning; few-shot learning; jamming recognition; model-agnostic meta-learning
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 07 Sep 2022 08:06
Last Modified: 23 Sep 2022 19:52

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