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

作　　者：Tao JIANG Wanqing CHEN Hangping ZHOU Jinyang HE Peihan QI 

机构地区：[1]School of Cyber Engineering,Xidian University,Xi’an 710126,China [2]State Key Laboratory of Integrate Service Networks,Xidian University,Xi’an 71007l,China [3]Guangzhou Institute of Technology,Xidian University,Guangzhou 510555,China

出　　处：《Chinese Journal of Electronics》2024年第3期721-731,共11页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.62171334);the Fundamental Research Funds for the Central Universities (Grant No.ZYTS23162)。

摘　　要：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.

关 键 词：Abnormal spectrum classification Auto Encoder SEMI-SUPERVISED DeepCluster 

分 类 号：TN92[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]