Academic Journal
KAN-ResNet-Enhanced Radio Frequency Fingerprint Identification with Zero-Forcing Equalization.
| Title: | KAN-ResNet-Enhanced Radio Frequency Fingerprint Identification with Zero-Forcing Equalization. |
|---|---|
| Authors: | Chen, Hongbo, Zhou, Ruohua, Yuan, Qingsheng, Guo, Ziye, Fu, Wei |
| Source: | Sensors (14248220); Apr2025, Vol. 25 Issue 7, p2222, 31p |
| Subject Terms: | Radio frequency identification systems, Internet security, Internet of things, Error rates, Wireless Internet |
| Abstract: | Radio Frequency Fingerprint Identification (RFFI) is a promising device authentication technique that utilizes inherent hardware flaws in transmitters to achieve device identification, thus effectively maintaining the security of the Internet of Things (IoT). However, time-varying channels degrade accuracy due to factors like device aging and environmental changes. To address this, we propose an RFFI method integrating Zero-Forcing (ZF) equalization and KAN-ResNet. Firstly, the Wi-Fi preamble signals under the IEEE 802.11 standard are Zero-Forcing equalized, so as to effectively reduce the interference of time-varying channels on RFFI. We then design a novel residual network, KAN-ResNet, which adds a KAN module on top of the traditional fully connected layer. The module combines the B-spline basis function and the traditional activation function Sigmoid Linear Unit (SiLU) to realize the nonlinear mapping of the complex function, which enhance the classification ability of the network for RFF features. In addition, to improve the generalization of the model, the grid of B-splines is dynamically updated and L1 regularization is introduced. Experiments show that on datasets collected 20 days apart, our method achieves 99.4% accuracy, reducing the error rate from 6.3% to 0.6%, outperforming existing models. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=14248220&ISBN=&volume=25&issue=7&date=20250401&spage=2222&pages=2222-2252&title=Sensors (14248220)&atitle=KAN-ResNet-Enhanced%20Radio%20Frequency%20Fingerprint%20Identification%20with%20Zero-Forcing%20Equalization.&aulast=Chen%2C%20Hongbo&id=DOI:10.3390/s25072222 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: edb DbLabel: Complementary Index An: 184439168 RelevancyScore: 984 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 983.703247070313 |
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| Items | – Name: Title Label: Title Group: Ti Data: KAN-ResNet-Enhanced Radio Frequency Fingerprint Identification with Zero-Forcing Equalization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Hongbo%22">Chen, Hongbo</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Ruohua%22">Zhou, Ruohua</searchLink><br /><searchLink fieldCode="AR" term="%22Yuan%2C+Qingsheng%22">Yuan, Qingsheng</searchLink><br /><searchLink fieldCode="AR" term="%22Guo%2C+Ziye%22">Guo, Ziye</searchLink><br /><searchLink fieldCode="AR" term="%22Fu%2C+Wei%22">Fu, Wei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sensors (14248220); Apr2025, Vol. 25 Issue 7, p2222, 31p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Radio+frequency+identification+systems%22">Radio frequency identification systems</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Error+rates%22">Error rates</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+Internet%22">Wireless Internet</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Radio Frequency Fingerprint Identification (RFFI) is a promising device authentication technique that utilizes inherent hardware flaws in transmitters to achieve device identification, thus effectively maintaining the security of the Internet of Things (IoT). However, time-varying channels degrade accuracy due to factors like device aging and environmental changes. To address this, we propose an RFFI method integrating Zero-Forcing (ZF) equalization and KAN-ResNet. Firstly, the Wi-Fi preamble signals under the IEEE 802.11 standard are Zero-Forcing equalized, so as to effectively reduce the interference of time-varying channels on RFFI. We then design a novel residual network, KAN-ResNet, which adds a KAN module on top of the traditional fully connected layer. The module combines the B-spline basis function and the traditional activation function Sigmoid Linear Unit (SiLU) to realize the nonlinear mapping of the complex function, which enhance the classification ability of the network for RFF features. In addition, to improve the generalization of the model, the grid of B-splines is dynamically updated and L1 regularization is introduced. Experiments show that on datasets collected 20 days apart, our method achieves 99.4% accuracy, reducing the error rate from 6.3% to 0.6%, outperforming existing models. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s25072222 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 2222 Subjects: – SubjectFull: Radio frequency identification systems Type: general – SubjectFull: Internet security Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Error rates Type: general – SubjectFull: Wireless Internet Type: general Titles: – TitleFull: KAN-ResNet-Enhanced Radio Frequency Fingerprint Identification with Zero-Forcing Equalization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Hongbo – PersonEntity: Name: NameFull: Zhou, Ruohua – PersonEntity: Name: NameFull: Yuan, Qingsheng – PersonEntity: Name: NameFull: Guo, Ziye – PersonEntity: Name: NameFull: Fu, Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 25 – Type: issue Value: 7 Titles: – TitleFull: Sensors (14248220) Type: main |
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