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Title:A representation learning approach to feature drift detection in wireless networks
Authors:ID Tziouvaras, Athanasios (Author)
ID Bertalanič, Blaž, Institut "Jožef Stefan" (Author)
ID Floros, George (Author)
ID Kolomvatsos, Kostas (Author)
ID Sarigiannidis, Panagiotis (Author)
ID Fortuna, Carolina, Institut "Jožef Stefan" (Author)
Files:URL URL - Source URL, visit https://ieeexplore.ieee.org/document/11311434
 
.pdf PDF - Presentation file, download (2,84 MB)
MD5: FB8DB764780A1DAE546E61439D5DBA20
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo IJS - Jožef Stefan Institute
Abstract: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.
Keywords:feature druft detection, fingerprinting, link anomaly detection
Publication version:Author Accepted Manuscript
Publication date:22.12.2025
Publisher:IEEE
Year of publishing:2025
Number of pages:str. 1-14
Numbering:Vol.
Source:ZDA
PID:20.500.12556/DiRROS-24986 New window
UDC:004.8
ISSN on article:2168-6750
DOI:10.1109/TETC.2025.3644604 New window
COBISS.SI-ID:263579395 New window
Note:Nasl. z nasl. zaslona; Soavtorja iz Slovenije: Blaž Bertalanič, Carolina Fortuna; Opis vira z dne 6. 1. 2026;
Publication date in DiRROS:07.01.2026
Views:56
Downloads:25
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Record is a part of a journal

Title:IEEE transactions on emerging topics in computing
Shortened title:IEEE trans. emerg. top. comput.
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2168-6750
COBISS.SI-ID:18656278 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0016-2019
Name:Komunikacijska omrežja in storitve

Funder:EC - European Commission
Project number:101096456
Name:An Artificial Intelligent Aided Unified Network for Secure Beyond 5G Long Term Evolution
Acronym:NANCY

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:22.12.2025
Applies to:AAM

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