20.500.12556/DiRROS-24986 A representation learning approach to feature drift detection in wireless networks Artificial Intelligence (AI) is foreseen to be a centerpiece in next generation wireless networks enabling ubiquitous communication as well as new services. However, in real deployment, feature distribution changes may degrade the performance of Artificial Intelligence (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 Multi-layer Perceptron (MLP) for designing the representation learning component, on Kolmogorov-Smirnov (KS) and Population Stability Index (PSI) 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. feature druft detection fingerprinting link anomaly detection true false true IEEE ZDA Angleški jezik Ni določen Neznano 2026-01-07 10:52:59 2026-01-07 10:53:00 2026-01-08 03:47:21 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Soavtorja iz Slovenije: Blaž Bertalanič, Carolina Fortuna; Opis vira z dne 6. 1. 2026; str. 1-14 Vol. [in press] 2025 0000-00-00 PostprintKoncna NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2025-12-22 004.8 2168-6750 10.1109/TETC.2025.3644604 263579395 https://ieeexplore.ieee.org/document/11311434 1 a8edaea5-ebae-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=37450 RAZ_Tziouvaras_Athanasios_2025.pdf RAZ_Tziouvaras_Athanasios_2025.pdf 1 FB8DB764780A1DAE546E61439D5DBA20 dc93c7ec9d190c4ed88d52a36e9d6ddcbd4c443ec8095369b03c58587b2fc729 30e50a1b-ebaf-11f0-94a7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=37451 Institut Jožef Stefan 0 0 0