Transfer Learning based Location-Aided Modulation Classification in Indoor Environments for Cognitive Radio Applications
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Date
2023-12
Authors
Tamizhelakkiya, K.
Gauni, S.
Chandhar, P.
ORCID
Advisor
Referee
Mark
Journal Title
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Volume Title
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Společnost pro radioelektronické inženýrství
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Abstract
Modulation classification is a crucial technique to utilize the unconsumed spectrum in Cognitive Radio (CR) and Dynamic Spectrum Access (DSA) systems to meet the required traffic demands for future-generation cellular networks. This paper presents an end-to-end experimental setup as a generic methodology to implement various Transfer Learning (TL) models in an indoor environment. This allows us to learn the features from multiple modulation signals to train and test the model. The performance evaluation of proposed TL models such as Convolutional Neural Network-Random Forest (CNN-RF), and Convolutional Long Short Term Deep Neural Network (CLDNN) -Random Forest (CLDNN-RF) have been thoroughly discussed. The result shows that the proposed TL models yield more than 90% classification accuracy for various modulation types. A proposed framework for location-specific TL model selection based on the maximum classification accuracy has been investigated.
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Citation
Radioengineering. 2023 vol. 32, č. 4, s. 531-541. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2023/23_04_0531_0541.pdf
https://www.radioeng.cz/fulltexts/2023/23_04_0531_0541.pdf
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Peer-reviewed
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Published version
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en