Laboratory access recognition based on MAML and radio frequency fingerprint
Abstract Addressing the efficiency and accuracy issues of radio frequency fingerprint recognition in laboratory access control, this paper presents a recognition model based on model-agnostic meta-learning and radio frequency fingerprint. By integrating short-time Fourier transform and constellation...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06812-w |
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| author | Qun Zhu Qingxin Lin |
| author_facet | Qun Zhu Qingxin Lin |
| author_sort | Qun Zhu |
| collection | DOAJ |
| description | Abstract Addressing the efficiency and accuracy issues of radio frequency fingerprint recognition in laboratory access control, this paper presents a recognition model based on model-agnostic meta-learning and radio frequency fingerprint. By integrating short-time Fourier transform and constellation diagram, the proposed approach significantly enhances identification performance. The experimental results show that in the simulation run experiment, the average recognition accuracy of the proposed model before and after training is 0.59 and 0.97, respectively. When the training sample size is 10 and 5, the average recognition efficiency of the proposed model is 93.80% and 90.15%, respectively. When the model adds the short-time Fourier transform and constellation diagram, its recognition accuracy and efficiency increase by 0.19 and 13.01%, respectively. In addition, in actual model performance experiments, the proposed model shows the most stable performance, with an average of 90.47% for recognition accuracy and 0.91 for efficiency. Moreover, the recognition accuracy of the model for different types of signals can reach up to 0.91. In environments with signal-to-noise ratios of 5 and 0, the average recognition efficiency of the proposed model is 83.20% and 62.00%, respectively. The original contributions of the study lie in the optimization of feature extraction and model training processes, which significantly enhance the accuracy and efficiency of radio frequency fingerprint recognition, especially in scenarios with limited sample sizes. This addresses the issues of low accuracy and poor efficiency in traditional radio frequency fingerprint models. The study is capable of improving the efficiency and accuracy of radio frequency fingerprint recognition and enhancing the level of laboratory safety management and control. |
| format | Article |
| id | doaj-art-3c9015ce3cbb4b9c838ae3e0fe0a8f90 |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-3c9015ce3cbb4b9c838ae3e0fe0a8f902025-08-20T02:19:57ZengSpringerDiscover Applied Sciences3004-92612025-04-017511310.1007/s42452-025-06812-wLaboratory access recognition based on MAML and radio frequency fingerprintQun Zhu0Qingxin Lin1Faculty of Tourism and Cultural Creativity, Fujian Polytechnic of Information TechnologyZhicheng College, Fuzhou UniversityAbstract Addressing the efficiency and accuracy issues of radio frequency fingerprint recognition in laboratory access control, this paper presents a recognition model based on model-agnostic meta-learning and radio frequency fingerprint. By integrating short-time Fourier transform and constellation diagram, the proposed approach significantly enhances identification performance. The experimental results show that in the simulation run experiment, the average recognition accuracy of the proposed model before and after training is 0.59 and 0.97, respectively. When the training sample size is 10 and 5, the average recognition efficiency of the proposed model is 93.80% and 90.15%, respectively. When the model adds the short-time Fourier transform and constellation diagram, its recognition accuracy and efficiency increase by 0.19 and 13.01%, respectively. In addition, in actual model performance experiments, the proposed model shows the most stable performance, with an average of 90.47% for recognition accuracy and 0.91 for efficiency. Moreover, the recognition accuracy of the model for different types of signals can reach up to 0.91. In environments with signal-to-noise ratios of 5 and 0, the average recognition efficiency of the proposed model is 83.20% and 62.00%, respectively. The original contributions of the study lie in the optimization of feature extraction and model training processes, which significantly enhance the accuracy and efficiency of radio frequency fingerprint recognition, especially in scenarios with limited sample sizes. This addresses the issues of low accuracy and poor efficiency in traditional radio frequency fingerprint models. The study is capable of improving the efficiency and accuracy of radio frequency fingerprint recognition and enhancing the level of laboratory safety management and control.https://doi.org/10.1007/s42452-025-06812-wRFFMAMLSTFTConstellation diagramLaboratory management |
| spellingShingle | Qun Zhu Qingxin Lin Laboratory access recognition based on MAML and radio frequency fingerprint Discover Applied Sciences RFF MAML STFT Constellation diagram Laboratory management |
| title | Laboratory access recognition based on MAML and radio frequency fingerprint |
| title_full | Laboratory access recognition based on MAML and radio frequency fingerprint |
| title_fullStr | Laboratory access recognition based on MAML and radio frequency fingerprint |
| title_full_unstemmed | Laboratory access recognition based on MAML and radio frequency fingerprint |
| title_short | Laboratory access recognition based on MAML and radio frequency fingerprint |
| title_sort | laboratory access recognition based on maml and radio frequency fingerprint |
| topic | RFF MAML STFT Constellation diagram Laboratory management |
| url | https://doi.org/10.1007/s42452-025-06812-w |
| work_keys_str_mv | AT qunzhu laboratoryaccessrecognitionbasedonmamlandradiofrequencyfingerprint AT qingxinlin laboratoryaccessrecognitionbasedonmamlandradiofrequencyfingerprint |