A target behavior pattern mining and abnormal behavior monitoring based on multidimensional similarity metric

In recent years, electromagnetic spectrum has become an indispensable national strategic resource in the information age and an important strategic support for the development of national informatization. With the rapid proliferation of wireless data today, the demand for electromagnetic spectrum is...

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Bibliographic Details
Published inWireless networks Vol. 29; no. 7; pp. 3027 - 3037
Main Authors Liu, Chang, Chen, Zhuo, Wu, Yonghao, Antypenko, Ruslan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2023
Springer Nature B.V
Subjects
Accuracy
Algorithms
Autoregressive models
Communications Engineering
Computer Communication Networks
Data mining
Electrical Engineering
Engineering
IT in Business
Networks
Pattern analysis
Similarity
Wireless networks
Seq2Seq
Similarity metric
Occupancy
Machine learning
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Summary:In recent years, electromagnetic spectrum has become an indispensable national strategic resource in the information age and an important strategic support for the development of national informatization. With the rapid proliferation of wireless data today, the demand for electromagnetic spectrum is also growing rapidly, and spectrum resources are limited, leading to the increasingly prominent problem of spectrum scarcity. In response to the traditional spectrum prediction methods for spectrum prediction with extended time and low accuracy, this paper analyzes the spectrum correlation of different channels that are in the same service, performs the similarity measure of frequency dimension data, and proves the correlation between the spectrum; and predicts the spectrum data with different time occupancy, and conducts deep mining of the spectrum target behavior from the time–frequency dimension. The experiments mainly use sequence to sequence (Seq2Seq) based Long Short-Term Memory (LSTM) to predict the data of each occupancy degree, and compare with the traditional LSTM network, Autoregressive Integrated Moving Average model (ARIMA), etc. The prediction accuracy of the method in this paper is higher. At the same time, the abnormal spectrum is monitored using the Mahalanobis distance algorithm, the detection accuracy reaches 100%.
ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-023-03270-3