LoRa radio frequency fingerprinting identification using a hybrid quantum-classical neural network

To Truong An, Simon L. Cotton, Junqing Zhang, Yuan Ding, Trung Q. Duong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Radio frequency fingerprint identification is a promising technique for device authentication that relies on the unique radio frequency fingerprint features caused by hardware impairments. Existing radio frequency fingerprint identification models usually contain a significant number of trainable parameters, making them undesirable for Internet of Things applications. In this paper, we augment a classical neural network by introducing an intermediary quantum neural network stage to enhance the authentication of Internet of Things devices using radio frequency fingerprint features. The model is based on the combination of quantum and classical machine learning and benefits from a significantly smaller number of trainable parameters. Empirical results show that our proposed model not only achieves a much smaller footprint (in terms of device memory) but also delivers competitive accuracy to conventional deep learning approaches. It therefore shows much promise as a solution for securing networks which feature resource-constrained Internet of Things devices.
Original languageEnglish
Title of host publication2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798331517786
ISBN (Print)9798331517793
DOIs
Publication statusPublished - 28 Nov 2024
Event2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall) - Washington DC, United States
Duration: 07 Oct 202410 Oct 2024

Publication series

Name IEEE Vehicular Technology Conference (VTC2024-Fall): Proceedings
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465

Conference

Conference2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)
Country/TerritoryUnited States
CityWashington DC
Period07/10/202410/10/2024

Keywords

  • Deep learning
  • device authentication
  • Hybrid quantum-classical machine learning
  • Internet of Things

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