Xiangwei Chen1, Zhijin Zhao1,2,*, Xueyi Ye1, Shilian Zheng2, Caiyi Lou2, Xiaoniu Yang2
Computer Systems Science and Engineering, Vol.46, No.2, pp. 1385-1400, 2023, DOI:10.32604/csse.2023.034543
Abstract Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms. However, there is no unsupervised interference signals recognition algorithm at present. In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed. Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning. In the second phase, the original dataset’s k-nearest neighbor set (KNNset) is designed… More >
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