A Representation Learning Approach to Feature Drift Detection in Wireless Networks
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911334109544448 |
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| author | Tziouvaras, Athanasios Bertalanic, Blaz Floros, George Kolomvatsos, Kostas Sarigiannidis, Panagiotis Fortuna, Carolina |
| author_facet | Tziouvaras, Athanasios Bertalanic, Blaz Floros, George Kolomvatsos, Kostas Sarigiannidis, Panagiotis Fortuna, Carolina |
| contents | AI is foreseen to be a centerpiece in next generation wireless networks enabling enabling ubiquitous communication as well as new services. However, in real deployment, feature distribution changes may degrade the performance of AI models and lead to undesired behaviors. To counter for undetected model degradation, we propose ALERT; a method that can detect feature distribution changes and trigger model re-training that works well on two wireless network use cases: wireless fingerprinting and link anomaly detection. ALERT includes three components: representation learning, statistical testing and utility assessment. We rely on MLP for designing the representation learning component, on Kolmogorov-Smirnov and Population Stability Index tests for designing the statistical testing and a new function for utility assessment. We show the superiority of the proposed method against ten standard drift detection methods available in the literature on two wireless network use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10325 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Representation Learning Approach to Feature Drift Detection in Wireless Networks Tziouvaras, Athanasios Bertalanic, Blaz Floros, George Kolomvatsos, Kostas Sarigiannidis, Panagiotis Fortuna, Carolina Machine Learning AI is foreseen to be a centerpiece in next generation wireless networks enabling enabling ubiquitous communication as well as new services. However, in real deployment, feature distribution changes may degrade the performance of AI models and lead to undesired behaviors. To counter for undetected model degradation, we propose ALERT; a method that can detect feature distribution changes and trigger model re-training that works well on two wireless network use cases: wireless fingerprinting and link anomaly detection. ALERT includes three components: representation learning, statistical testing and utility assessment. We rely on MLP for designing the representation learning component, on Kolmogorov-Smirnov and Population Stability Index tests for designing the statistical testing and a new function for utility assessment. We show the superiority of the proposed method against ten standard drift detection methods available in the literature on two wireless network use cases. |
| title | A Representation Learning Approach to Feature Drift Detection in Wireless Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.10325 |