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Radio frequency fingerprint collaborative intelligent identification using incremental learning
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Liu, Mingqian, Wang, Jiakun, Zhao, Nan, Chen, Yunfei, Song, Hao and Yu, F. Richard (2022) Radio frequency fingerprint collaborative intelligent identification using incremental learning. IEEE Transactions on Network Science and Engineering, 9 (5). pp. 3222-3233. doi:10.1109/TNSE.2021.3103805 ISSN 2327-4697.
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Official URL: https://doi.org/10.1109/TNSE.2021.3103805
Abstract
For distributed sensor systems using neural networks, each sub-network has a different electromagnetic environment, and these recognition accuracy is also different. In this paper, we propose a distributed sensor system using incremental learning to solve the problem of radio frequency fingerprint identification. First, the intelligent representation of the received signal is linearly fused into a four-channel image. Then, convolutional neural network is trained by using the existing data to obtain the preliminary model of the network, and decision fusion is used to solve the problem in the distributed system. Finally, using new data, instead of retraining the model, we employ incremental learning by fine-tuning the preliminary model. The proposed method can significantly reduce the training time and is adaptive to streaming data. Extensive experiments show that the proposed method is computationally efficient, and also has satisfactory recognition accuracy, especially at low signal-to-noise ratio (SNR) regime.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Radio frequency identification systems, Neural networks (Computer science), Pattern perception, Machine learning, Multimedia systems, Computer networks | |||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Network Science and Engineering | |||||||||||||||
| Publisher: | IEEE Computer Society | |||||||||||||||
| ISSN: | 2327-4697 | |||||||||||||||
| Official Date: | September 2022 | |||||||||||||||
| Dates: |
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| Volume: | 9 | |||||||||||||||
| Number: | 5 | |||||||||||||||
| Page Range: | pp. 3222-3233 | |||||||||||||||
| DOI: | 10.1109/TNSE.2021.3103805 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| Date of first compliant deposit: | 9 August 2021 | |||||||||||||||
| Date of first compliant Open Access: | 9 August 2021 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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