SICE Journal of Control, Measurement, and System Integration
Online ISSN : 1884-9970
Print ISSN : 1882-4889
ISSN-L : 1882-4889
Special Issue on Fundamentals and Applications of Smart Sensing
Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio
Kenta ASAKURAHaruhisa ICHIKAWAYuusuke KAWAKITA
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2019 Volume 12 Issue 4 Pages 134-141

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Abstract

The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal's spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is effective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods.

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© 2019 The Society of Instrument and Control Engineers
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