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Tao JIANG, Wanqing CHEN, Hangping ZHOU, et al., “Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–11, 2024 doi: 10.23919/cje.2022.00.395
Citation: Tao JIANG, Wanqing CHEN, Hangping ZHOU, et al., “Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–11, 2024 doi: 10.23919/cje.2022.00.395

Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning

doi: 10.23919/cje.2022.00.395
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  • Author Bio:

    Tao JIANG received the Ph.D. degree in cryptology from the School of Telecommunications Engineering, Xidian University, Xi’an, China, in 2016. He is currently an Associate Professor with the School of Cyber Engineering, Xidian University. His research interests include applied cryptography and artificial intelligence security.(Email: taojiang@xidian.edu.cn)

    Wanqing CHEN was born in Hubei Province, China, in 1998. She received the B.E. degree in communication engineering from Three Gorges University, Yichang, China, in 2020. She is currently an M.S. candidate in Xidian University. Her main research interests are deep learning, image classification, and wireless spectrum identification. (Email: wqchen@stu.xidian.edu.cn)

    Hangping ZHOU was born in Shandong Province, China, in 1995. He received the B.E. degree in software engineering from Shandong Jianzhu University, Jinan, China, in 2019. He is currently an M.S. candidate in Xidian University, Xi’an, China. His main research interests are deep learning, adversarial sample attack and prevention, and artificial intelligence security. (Email: hangpingzhou@stu.xidian.edu.cn)

    Jinyang HE was born in Shaanxi Province, China, in 1998. He received the B.E. degree in geological engineering from China University of Mining and Technology, Xuzhou, China, in 2020. He is currently an M.S. candidate in Xidian University, Xi’an, China. His main research interests are deep learning and wireless spectrum anomaly detection. (Email: yanghe3586@outlook.com)

    Peihan QI was born in Henan Province, China, in 1986. He received the B.S. degree from Chang’an University, Xi’an, China, in 2006, and the M.S. degree and the Ph.D. degree from Xidian University, Xi’an, in 2011 and 2014, respectively. He is currently a Professor with the School of Telecommunications Engineering, Xidian University. His research interests include compressed sensing, spectrum sensing in cognitive radio networks, and high-speed digital signal processing. (Email: phqi@xidian.edu.cn)

  • Corresponding author: Email: phqi@xidian.edu.cn
  • Received Date: 2022-11-18
  • Accepted Date: 2023-06-16
  • Available Online: 2023-08-23
  • In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with interference-to-signal ratios, we proposes a semi-supervised classification of abnormal spectrum signals (SSC-ASS), aimed at addressing some of the challenges in abnormal spectrum signal (ASS) classification tasks. A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data, but instead achieves high-precision classification of ASSs using only a small number of labeled data. Furthermore, the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals, thereby improving classification accuracy. Specifically, SSC-ASS uses a memory AutoEncoder module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction error. Additionally, SSC-ASS combines convolutional neural network and the K-means using a DeepCluster framework to fully utilize the unlabeled data. Furthermore, SSC-ASS also utilizes pre-training, category mean memory module and replaces pseudo-labels to further improve the classification accuracy of ASSs. And we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.
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