{"title":"A Fast Cross-Band Spectrum Anomaly Detection Algorithm Based on Meta-Learning","authors":"Chung Peng, Mengbo Zhang, Weilin Hu, Lunwen Wang","doi":"10.1109/ICSP54964.2022.9778699","DOIUrl":null,"url":null,"abstract":"Spectrum anomaly detection is an important research topic in cognitive radio. It can detect anomalies by predicting differences between the actual data. Existing deep learning-based spectral anomaly detection algorithms use a lot of training data. Due to the difference in frequency band, the detection model cannot be used directly across frequency bands. In order to solve this problem, a cross-band spectral anomaly detection method based on meta-learning is studied in this paper. Firstly, the data of different frequency bands are analyzed by using the pre-training of InceptionV3 to clarify the differences between different frequency bands. Secondly, a meta-learning data set is constructed and the optimal distribution of model parameters is found through the meta-learning training model. Finally, a small amount of target band data is used to fine-tune the model to detect anomalies in the target band. The experimental findings suggest that the proposed method is more stable than transfer learning and can detect cross-band anomalies with less target band data.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSP54964.2022.9778699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Spectrum anomaly detection is an important research topic in cognitive radio. It can detect anomalies by predicting differences between the actual data. Existing deep learning-based spectral anomaly detection algorithms use a lot of training data. Due to the difference in frequency band, the detection model cannot be used directly across frequency bands. In order to solve this problem, a cross-band spectral anomaly detection method based on meta-learning is studied in this paper. Firstly, the data of different frequency bands are analyzed by using the pre-training of InceptionV3 to clarify the differences between different frequency bands. Secondly, a meta-learning data set is constructed and the optimal distribution of model parameters is found through the meta-learning training model. Finally, a small amount of target band data is used to fine-tune the model to detect anomalies in the target band. The experimental findings suggest that the proposed method is more stable than transfer learning and can detect cross-band anomalies with less target band data.