Relevant bibliographies by topics / Radio Frequency Fingerprint

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Radio Frequency Fingerprint'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Radio Frequency Fingerprint"

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Deng, Shouyun, Zhitao Huang, Xiang Wang, and Guangquan Huang. "Radio Frequency Fingerprint Extraction Based on Multidimension Permutation Entropy." International Journal of Antennas and Propagation 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1538728.

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Radio frequency fingerprint (RF fingerprint) extraction is a technology that can identify the unique radio transmitter at the physical level, using only external feature measurements to match the feature library. RF fingerprint is the reflection of differences between hardware components of transmitters, and it contains rich nonlinear characteristics of internal components within transmitter. RF fingerprint technique has been widely applied to enhance the security of radio frequency communication. In this paper, we propose a new RF fingerprint method based on multidimension permutation entropy. We analyze the generation mechanism of RF fingerprint according to physical structure of radio transmitter. A signal acquisition system is designed to capture the signals to evaluate our method, where signals are generated from the same three Anykey AKDS700 radios. The proposed method can achieve higher classification accuracy than that of the other two steady-state methods, and its performance under different SNR is evaluated from experimental data. The results demonstrate the effectiveness of the proposal.
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Zhang, Yulan, Jun Hu, Rundong Jiang, Zengrong Lin, and Zengping Chen. "Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism." Entropy 26, no. 1 (December 27, 2023): 29. http://dx.doi.org/10.3390/e26010029.

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With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.
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Shen, Danyao, Fengchao Zhu, Zhanpeng Zhang, and Xiaodong Mu. "Radio Frequency Fingerprint Identification Based on Metric Learning." International Journal of Information Technologies and Systems Approach 16, no. 3 (April 13, 2023): 1–13. http://dx.doi.org/10.4018/ijitsa.321194.

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With the popularization of the internet of things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. However, the problems of poor performance in a highly noisy environment and less consideration of calculation resources are urgent to be resolved in a practical RFF application domain. The authors propose a new RFF identification method based on metric learning. They used power spectrum density (PSD) to extract the RFF from the nonlinearity of the RF front end. Then they adopted the large margin nearest neighbor (LMNN) classification algorithm to identify eight software-defined radio (SDR) devices. Different from existing RFF identification algorithms, the proposed LMNN method is more general and can learn the optimal metric from the wireless communication environment. Furthermore, they propose a new training and test strategy based on mixed SNR, which significantly improves the performance of conventional low-complexity RFF identification methods. Experimental results show that the proposed method can achieve 99.8% identification accuracy with 30dB SNR and 96.83% with 10dB SNR. In conclusion, the study demonstrates the effectiveness of the proposed method in recognition efficiency and computational complexity.
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Sun, Haotai, Xiaodong Zhu, Yuanning Liu, and Wentao Liu. "Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method." Sensors 20, no. 10 (May 24, 2020): 2981. http://dx.doi.org/10.3390/s20102981.

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Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.
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zhuo, Fei, Yuanling Huang, and Jian chen. "Radio Frequency Fingerprint Extraction of Radio Emitter Based on I/Q Imbalance." Procedia Computer Science 107 (2017): 472–77. http://dx.doi.org/10.1016/j.procs.2017.03.092.

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Rehman, Saeed Ur, Shafiq Alam, and Iman T. Ardekani. "An Overview of Radio Frequency Fingerprinting for Low-End Devices." International Journal of Mobile Computing and Multimedia Communications 6, no. 3 (July 2014): 1–21. http://dx.doi.org/10.4018/ijmcmc.2014070101.

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RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. A masquerading or impersonation attacks can be prevented by establishing the identity of wireless transmitter using unique transmitter RF fingerprint. Unique RF fingerprints are attributable to the analog components (digital-to-analog converters, band-pass filters, frequency mixers and power amplifiers) present in the RF front ends of transmitters. Most of the previous researches have reported promising results with an accuracy of up to 99% using high-end receivers (e.g. Giga-sampling rate oscilloscopes, spectrum and vector signal analysers) to validate the proposed techniques. However, practical implementation of RF fingerprinting would require validation with low-end (low-cost) devices that also suffers from impairments due to the presence of analog components in the front end of its receiver. This articles provides the analysis and implementation of RF fingerprinting using low-cost receivers and challenges associated with it.
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Shen, Guanxiong, Junqing Zhang, Alan Marshall, Linning Peng, and Xianbin Wang. "Radio Frequency Fingerprint Identification for LoRa Using Deep Learning." IEEE Journal on Selected Areas in Communications 39, no. 8 (August 2021): 2604–16. http://dx.doi.org/10.1109/jsac.2021.3087250.

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Chang, Jiale, Zhengxiao Zhou, Siya Mi, and Yu Zhang. "Radio frequency fingerprint recognition method based on prior information." Computers and Electrical Engineering 120 (December 2024): 109684. http://dx.doi.org/10.1016/j.compeleceng.2024.109684.

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Htun, Myo Thet. "Compact and Robust MFCC-based Space-Saving Audio Fingerprint Extraction for Efficient Music Identification on FM Broadcast Monitoring." Journal of ICT Research and Applications 16, no. 3 (December 27, 2022): 226–42. http://dx.doi.org/10.5614/itbj.ict.res.appl.2022.16.3.3.

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The Myanmar music industry urgently needs an efficient broadcast monitoring system to solve copyright infringement issues and illegal benefit-sharing between artists and broadcasting stations. In this paper, a broadcast monitoring system is proposed for Myanmar FM radio stations by utilizing space-saving audio fingerprint extraction based on the Mel Frequency Cepstral Coefficient (MFCC). This study focused on reducing the memory requirement for fingerprint storage while preserving the robustness of the audio fingerprints to common distortions such as compression, noise addition, etc. In this system, a three-second audio clip is represented by a 2,712-bit fingerprint block. This significantly reduces the memory requirement when compared to Philips Robust Hashing (PRH), one of the dominant audio fingerprinting methods, where a three-second audio clip is represented by an 8,192-bit fingerprint block. The proposed system is easy to implement and achieves correct and speedy music identification even on noisy and distorted broadcast audio streams. In this research work, we deployed an audio fingerprint database of 7,094 songs and broadcast audio streams of four local FM channels in Myanmar to evaluate the performance of the proposed system. The experimental results showed that the system achieved reliable performance.
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Zhang, Junqing, Roger Woods, Magnus Sandell, Mikko Valkama, Alan Marshall, and Joseph Cavallaro. "Radio Frequency Fingerprint Identification for Narrowband Systems, Modelling and Classification." IEEE Transactions on Information Forensics and Security 16 (2021): 3974–87. http://dx.doi.org/10.1109/tifs.2021.3088008.

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Dissertations / Theses on the topic "Radio Frequency Fingerprint"

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Chillet, Alice. "Sensitive devices Identification through learning of radio-frequency fingerprint." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS051.

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L’identification de dispositifs dits sensibles est soumise à différentes contraintes de sécurité ou de consommation d’énergie, ce qui rend les méthodes d’identification classiques peu adaptées. Pour répondre à ces contraintes, il est possible d’utiliser les défauts intrinsèques de la chaîne de transmission des dispositifs pour les identifier. Ces défauts altèrent le signal transmis et créent alors une signature par nature unique et non reproductible appelée empreinte Radio Fréquence (RF). Pour identifier un dispositif grâce à son empreinte RF, il est possible d’utiliser des méthodes d’estimation d’imperfections pour extraire une signature qui peut être utilisée par un classifieur, ou bien d’utiliser des méthodes d’apprentissage telles que les réseaux de neurones. Toutefois, la capacité d’un réseau de neurones à reconnaître les dispositifs dans un contexte particulier dépend fortement de la base de données d’entraînement. Dans cette thèse, nous proposons un générateur de bases de données virtuelles basé sur des modèles de transmission et d’imperfections RF. Ces bases de données virtuelles permettent de mieux comprendre les tenants et aboutissants de l’identification RF et de proposer des solutions pour rendre l’identification plus robuste. Dans un second temps, nous nous intéressons aux problématiques de complexité de la solution d’identification via deux axes. Le premier consiste à utiliser des graphes programmables intriqués, qui sont des modèles d’apprentissage par renforcement, basés sur des techniques d’évolution génétique moins complexes que les réseaux de neurones. Le second axe propose l’utilisation de l’élagage sur des réseaux de neurones de la littérature pour réduire la complexité de ces derniers
Identifying so-called sensitive devices is subject to various security or energy consumption constraints, making conventional identification methods unsuitable. To meet these constraints, it is possible to use intrinsic faults in the device’s transmission chain to identify them. These faults alter the transmitted signal, creating an inherently unique and non-reproducible signature known as the Radio Frequency (RF) fingerprint. To identify a device using its RF fingerprint, it is possible to use imperfection estimation methods to extract a signature that can be used by a classifier, or to use learning methods such as neural networks. However, the ability of a neural network to recognize devices in a particular context is highly dependent on the training database. This thesis proposes a virtual database generator based on RF transmission and imperfection models. These virtual databases allow us to better understand the ins and outs of RF identification and to propose solutions to make identification more robust. Secondly, we are looking at the complexity of the identification solution in two ways. The first involves the use of intricate programmable graphs, which are reinforcement learning models based on genetic evolution techniques that are less complex than neural networks. The second is to use pruning on neural networks found in the literature to reduce their complexity
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Maime, Ratakane Baptista. "CHALLENGES AND OPPORTUNITIES OF ADOPTING MANAGEMENT INFORMATION SYSTEMS (MIS) FOR PASSPORT PROCESSING: COMPARATIVE STUDY BETWEEN LESOTHO AND SOUTH AFRICA." Thesis, Central University of Technology, Free State. Business Administration, 2014. http://hdl.handle.net/11462/237.

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Thesis ( M. Tech. (Business Administration )) - Central University of Technology, Free State, 2014
Fast and secure public service delivery is not only a necessity, but a compulsory endeavour. However, it is close to impossible to achieve such objectives without the use of Information Technology (IT). It is correspondingly important to find proper sustainability frameworks of technology. Organisations do not only need technology for efficient public service; the constant upgrading of systems and cautious migration to the newest IT developments is also equally indispensable in today’s dynamic technological world. Conversely, countries in Africa are always lagging behind in technological progresses. Such deficiencies have been identified in the passport processing of Lesotho and South Africa, where to unequal extents, problems related to systems of passport production have contributed to delays and have become fertile grounds for corrupt practices. The study seeks to identify the main impediments in the adoption of Management Information Systems (MIS) for passport processing. Furthermore, the study explores the impact MIS might have in attempting to combat long queues and to avoid long waiting periods – from application to issuance of passports to citizens. The reasonable time frame between passport application and issuance, and specific passport management systems, have been extensively discussed along with various strategies that have been adopted by some of the world’s first movers in modern passport management technologies. In all cases and stages of this research, Lesotho and South Africa are compared. The research approach of the study was descriptive and explorative in nature. As a quantitative design, a structured questionnaire was used to solicit responses in Lesotho and South Africa. It was established that both Lesotho and South Africa have somewhat similar problems – although, to a greater extent, Lesotho needs much more urgent attention. Although the processes of South Africa need to be improved, the Republic releases a passport much faster and more efficiently than Lesotho. Economic issues are also revealed by the study as unavoidable factors that always affect technological developments in Africa. The study reveals that the latest MIS for passport processing has facilitated modern, automated border-control systems and resultant e-passports that incorporate more biometric information of citizens to passports – thanks to modern RFID technologies. One can anticipate that this study will provide simple, affordable and secure IT solutions for passport processing. Key words: Information Technology (IT); Management Information Systems (MIS); E-Government; E-Passport; Biometrics; and RFID.
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Campos, Rafael Saraiva. "Localização de Terminais Móveis utilizando Correlação de Assinaturas de Rádio-Frequência." Universidade do Estado do Rio de Janeiro, 2010. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=7542.

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Nesta dissertação são analisados métodos de localização baseados na rede, com destaque para os métodos de correlação de assinaturas de rádio-frequência (DCM - Database Correlation Methods). Métodos baseados na rede não requerem modificações nos terminais móveis (MS - Mobile Stations), sendo portanto capazes de estimar a localização de MS legados, i.e., sem suporte específico a posicionamento. Esta característica, associada a alta disponibilidade e precisão dos métodos DCM, torna-os candidatos viáveis para diversas aplicações baseadas em posição, e em particular para a localização de chamadas para números de emergência - polícia, defesa civil, corpo de bombeiros, etc. - originadas de telefones móveis celulares. Duas técnicas para diminuição do tempo médio para produção de uma estimativa de posição são formuladas: a filtragem determinística e a busca otimizada utilizando algoritmos genéticos. Uma modificação é realizada nas funções de avaliação utilizadas em métodos DCM, inserindo um fator representando a inacurácia intrínseca às medidas de nível de sinal realizadas pelos MS. As modificações propostas são avaliadas experimentalmente em redes de telefonia móvel celular de segunda e terceira gerações em ambientes urbanos e suburbanos, assim como em redes locais sem fio em ambiente indoor. A viabilidade da utilização de bancos de dados de correlação (CDB - Correlation Database) construídos a partir de modelagem de propagação é analisada, bem como o efeito da calibração de modelos de propagação empíricos na precisão de métodos DCM. Um dos métodos DCM propostos, utilizando um CDB calibrado, teve um desempenho superior ao de vários outros métodos DCM publicados na literatura, atingindo em área urbana a precisão exigida dos métodos baseados na rede pela regulamentação FCC (Federal Communications Commission) para o serviço E911 (Enhanced 911 ).
This work analyzes network based positioning methods, in particular the fingerprinting or database correlation methods. Network based methods do not require mobile station upgrading or replacement, thereby being capable of locating legacy mobile stations, i.e., without any specific positioning related features. This characteristic, coupled with the high availability and precision of fingerprinting methods, make them viable candidates for several location based applications, especially for the positioning of cellular mobile phones originating emergency calls - for police, fire brigade, etc. Two techniques to reduce the average positioning fix time are proposed: deterministic filtering and genetic algorithms optimized search. A modification is proposed in database correlation methods evaluation functions, by inserting a factor representing the inherent inaccuracy in the signal strength measurement made by the mobile station. The proposed improvements are experimentally evaluated in second and third generation cellular networks in urban and suburban environments, as well as in indoor wireless local area networks. The viability of using correlation databases built from propagation modeling is evaluated, as well as the effect of empirical propagation models calibration in the fingerprinting location precision. One of the proposed fingerprinting techniques, using a calibrated correlation database, achieved a performance superior to several other published fingerprinting methods, reaching in an urban area the precision requirements set by the Federal Communications Commission for network based methods providing the Enhanced 911 emergency location service.
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Books on the topic "Radio Frequency Fingerprint"

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Conan, Doyle A. The Return of Sherlock Holmes: Twelve BBC Radio 4 Full-Cast Dramatisations. BBC Audio, 2018.

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Book chapters on the topic "Radio Frequency Fingerprint"

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Li, Zhe, Yanxin Yin, and Lili Wu. "Radio Frequency Fingerprint Identification Method in Wireless Communication." In Machine Learning and Intelligent Communications, 195–202. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73564-1_19.

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Li, Hao, Yu Tang, Di Lin, Yuan Gao, and Jiang Cao. "A Survey of Few-Shot Learning for Radio Frequency Fingerprint Identification." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 433–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90196-7_37.

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Yang, Ning, and Yueyu Zhang. "A Radio Frequency Fingerprint Extraction Method Based on Cluster Center Difference." In Communications in Computer and Information Science, 282–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3095-7_22.

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Zhu, Q. C., X. O. Song, N. Lei, F. L. Qi, K. X. Liu, S. Y. Li, and Z. Y. Zhang. "Design of Single Radio Frequency Fingerprint Identification Algorithm for Aviation Equipment." In Advances in Intelligent Networking and Collaborative Systems, 475–84. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40971-4_45.

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Wang, Jijun, Ling Zhuang, Weihua Cheng, Chao Xu, Xiaohu Wu, and Zheying Zhang. "Analysis of Classification Methods Based on Radio Frequency Fingerprint for Zigbee Devices." In Advances in Intelligent Systems and Computing, 121–32. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6861-5_11.

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Li, Hongguang, Ying Guo, Zisen Qi, Ping Sui, and Linghua Su. "Fingerprint Feature Recognition of Frequency Hopping Radio with FCBF-NMI Feature Selection." In Lecture Notes in Electrical Engineering, 819–31. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_96.

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Zhang, Shunliang, Jing Li, and Xiaolei Guo. "Deep Learning Based Radio Frequency Fingerprint Identification by Exploiting Spatial Stereoscopic Features." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 511–19. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63992-0_34.

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Jayakumar, Sundaram, and Chandramohan Senthilkumar. "Biometric Fingerprints Based Radio Frequency Identification." In Intelligence and Security Informatics, 666–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427995_99.

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Rehman, Saeed ur, Shafiq Alam, and Iman T. Ardekani. "Security of Wireless Devices using Biological-Inspired RF Fingerprinting Technique." In Advances in Data Mining and Database Management, 311–30. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6078-6.ch015.

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Radio Frequency (RF) fingerprinting is a security mechanism inspired by biological fingerprint identification systems. RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. RF fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and using a classifier to correlate the received RF fingerprint with each profile RF fingerprint stored in the database. Unlike a human biological fingerprint, RF fingerprint of a wireless device changes with the received Signal to Noise Ratio (SNR) and varies due to mobility of the transmitter/receiver and environment. The variations in the features of RF fingerprints affect the classification results of the RF fingerprinting. This chapter evaluates the performance of the KNN and neural network classification for varying SNR. Performance analysis is performed for three scenarios that correspond to the situation, when either transmitter or receiver is mobile, and SNR changes from low to high or vice versa.
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Li, Zhongliang, Chunlong He, Riqing Liao, and Chiya Zhang. "Lightweight Radio Frequency Fingerprint Identification for LoRa." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia231237.

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Radio frequency fingerprinting is a key technology that plays an important role in enhancing the security of Internet-of-Things applications. In this paper, we present a new radio frequency fingerprinting system based on a novel feature extraction technique. We first convert the collected steady-state signals to grayscale images by byte without the need for any prior knowledge. Next, the collected data is fed into a lightweight neural network called MobileNet for training and classification. To evaluate the performance of the proposed system, we then conduct experiments with 10 Long Range (LoRa) devices and a general software radio receiver. Experimental results show that the proposed model outperforms some mainstream models. Moreover, we input mobile phone device data into our system. Experimental results demonstrate that our proposed model can achieve a significant classification accuracy of 99.23%.
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Conference papers on the topic "Radio Frequency Fingerprint"

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He, Yixin, Ying Ma, Ruiqi Qian, Yanqing Zhao, Haichuan Ding, and Jianping An. "Open-Set Long-Tailed Radio Frequency Fingerprint Identification." In 2024 IEEE/CIC International Conference on Communications in China (ICCC), 1543–48. IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10681794.

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Bothereau, Emma, Alice Chillet, Robin Gerzaguet, Matthieu Gautier, and Olivier Berder. "Investigating Sparse Neural Networks for Radio Frequency Fingerprint Identification." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 1–6. IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10757525.

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Wang, Hanbo, and Jian Wang. "Collaborative Radio Frequency Fingerprint Identification Using Dual-Channel Parallel CNN." In 2024 International Conference on Ubiquitous Communication (Ucom), 351–55. IEEE, 2024. http://dx.doi.org/10.1109/ucom62433.2024.10695867.

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Zhang, Yali, Nan Liu, Zhiwen Pan, and Xiaohu You. "Radio Frequency Fingerprint Identification in Low SNR Based on SCUNet." In 2024 IEEE/CIC International Conference on Communications in China (ICCC), 313–18. IEEE, 2024. http://dx.doi.org/10.1109/iccc62479.2024.10681757.

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Sun, Xuemin, Qing Wang, Zhiming Zhan, Xiaofeng Liu, Haozhi Wang, Qi Chen, and Yifang Zhang. "RDAS-RFFI: Robust Differentiable Architecture Search for Radio Frequency Fingerprint Identification." In 2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops), 424–29. IEEE, 2024. http://dx.doi.org/10.1109/icccworkshops62562.2024.10693753.

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Wang, Zhaorui, Xu Shi, Xiangyang Hua, Yang Sun, and Dongming Li. "Robust Radio Frequency Fingerprint Identification for UAVs During Fast Fading Channels." In 2024 3rd International Symposium on Aerospace Engineering and Systems (ISAES), 196–201. IEEE, 2024. http://dx.doi.org/10.1109/isaes61964.2024.10751244.

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Wang, Min, Linning Peng, Lingnan Xie, Junqing Zhang, Ming Liu, and Hua Fu. "Design of Noise Robust Open-Set Radio Frequency Fingerprint Identification Method." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/infocomwkshps61880.2024.10620671.

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Li, Xiang, Yu Tang, Erkang Li, and Di Lin. "Unsupervised Identification Method of Radio-Frequency Fingerprint Based on Deep Clustering." In 2024 4th International Conference on Intelligent Technology and Embedded Systems (ICITES), 136–41. IEEE, 2024. https://doi.org/10.1109/icites62688.2024.10777458.

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Liu, Weicheng, Yunsong Huang, and Hui-Ming Wang. "A Secure and Efficient Federated Learning Framework for Radio Frequency Fingerprint Recognition." In 2024 International Conference on Ubiquitous Communication (Ucom), 416–20. IEEE, 2024. http://dx.doi.org/10.1109/ucom62433.2024.10695904.

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Wu, Jiaming, Yan Zhang, Kaien Zhang, Zunwen He, and Wancheng Zhang. "A Receiver-Agnostic Radio Frequency Fingerprint Identification Approach in Low SNR Scenarios." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 1–5. IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10757464.

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