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Radio frequency fingerprint collaborative intelligent blind identification for green radios
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Liu, Mingqian, Liu, Chunheng, Chen, Yunfei, Yan, Zhiwen and Zhao, Nan (2023) Radio frequency fingerprint collaborative intelligent blind identification for green radios. IEEE Transactions on Green Communications and Networking, 7 (2). pp. 940-949. doi:10.1109/TGCN.2022.3185045 ISSN 2473-2400.
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WRAP-Radio-frequency-fingerprint-collaborative-intelligent-blind-identification-for-green-radios-Chen-22.pdf - Accepted Version - Requires a PDF viewer. Download (600Kb) | Preview |
Official URL: https://doi.org/10.1109/TGCN.2022.3185045
Abstract
Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. Moreover, a distributed federated learning system is used to solve the problem of insufficient number of local training samples for a multi-party joint training model without exchanging the original data of the samples. The federated learning center receives the network parameters uploaded by all local models for aggregation, and feeds the aggregated parameters back to each local model for a global update. The proposed blind identification method requires less information and no training sequences and pilots. Thus, it achieves energy-efficiency and spectrum-efficiency. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radios.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Radio frequency identification systems, Deep learning (Machine learning), Neural networks (Computer science) | |||||||||||||||
Journal or Publication Title: | IEEE Transactions on Green Communications and Networking | |||||||||||||||
Publisher: | IEEE | |||||||||||||||
ISSN: | 2473-2400 | |||||||||||||||
Official Date: | June 2023 | |||||||||||||||
Dates: |
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Volume: | 7 | |||||||||||||||
Number: | 2 | |||||||||||||||
Page Range: | pp. 940-949 | |||||||||||||||
DOI: | 10.1109/TGCN.2022.3185045 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2022 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: | 20 June 2022 | |||||||||||||||
Date of first compliant Open Access: | 21 June 2022 | |||||||||||||||
RIOXX Funder/Project Grant: |
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