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Publikationstyp: Konferenz: Paper
Art der Begutachtung: Peer review (Publikation)
Titel: Towards data analytics based PIM detection in wireless networks
Autor/-in: Deniz, Eren
Cantali, Gokcan
Ozay, Ozcan
Yildirim, Onur
Gür, Gürkan
Alagoz, Fatih
et. al: No
DOI: 10.1109/CAMAD59638.2023.10478405
10.21256/zhaw-31089
Tagungsband: 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Seite(n): 1
Seiten bis: 6
Angaben zur Konferenz: 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Edinburgh, United Kingdom, 6-8 November 2023
Erscheinungsdatum: 27-Mär-2024
Verlag / Hrsg. Institution: IEEE
ISBN: 979-8-3503-0349-0
Sprache: Englisch
Schlagwörter: Passive intermodulation (PIM); Anomaly detection; Quality of service (QoS); Radio access network (RAN)
Fachgebiet (DDC): 004: Informatik
Zusammenfassung: Passive Intermodulation (PIM) is a physical layer Radio Access Network (RAN) problem observed in both 4G and 5G networks. It is caused by internal physical processes such as inferior cabling or rusting, and external factors such as metallic obstacles in the radio propagation path. PIM degrades the user experience and radio resource efficiency while leading to an operation overhead for detecting and mitigating it on the operator side. Nevertheless, current solutions for PIM typically rely on costly hardware and site visit-based investigation by technicians. This work proposes a Machine Learning (ML) based PIM detection scheme for identifying PIM problems in RAN sites. Our approach relies on network KPI data already collected in the infrastructure for various purposes, including network monitoring, performance control, and maintenance. We investigate the performance of our proposed technique using empirical data collected from actual network cells.
URI: https://digitalcollection.zhaw.ch/handle/11475/31089
Volltext Version: Akzeptierte Version
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: School of Engineering
Organisationseinheit: Institut für Informatik (InIT)
Enthalten in den Sammlungen:Publikationen School of Engineering

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Deniz, E., Cantali, G., Ozay, O., Yildirim, O., Gür, G., & Alagoz, F. (2024). Towards data analytics based PIM detection in wireless networks [Conference paper]. 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 1–6. https://doi.org/10.1109/CAMAD59638.2023.10478405
Deniz, E. et al. (2024) ‘Towards data analytics based PIM detection in wireless networks’, in 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, pp. 1–6. Available at: https://doi.org/10.1109/CAMAD59638.2023.10478405.
E. Deniz, G. Cantali, O. Ozay, O. Yildirim, G. Gür, and F. Alagoz, “Towards data analytics based PIM detection in wireless networks,” in 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Mar. 2024, pp. 1–6. doi: 10.1109/CAMAD59638.2023.10478405.
DENIZ, Eren, Gokcan CANTALI, Ozcan OZAY, Onur YILDIRIM, Gürkan GÜR und Fatih ALAGOZ, 2024. Towards data analytics based PIM detection in wireless networks. In: 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Conference paper. IEEE. 27 März 2024. S. 1–6. ISBN 979-8-3503-0349-0
Deniz, Eren, Gokcan Cantali, Ozcan Ozay, Onur Yildirim, Gürkan Gür, and Fatih Alagoz. 2024. “Towards Data Analytics Based PIM Detection in Wireless Networks.” Conference paper. In 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 1–6. IEEE. https://doi.org/10.1109/CAMAD59638.2023.10478405.
Deniz, Eren, et al. “Towards Data Analytics Based PIM Detection in Wireless Networks.” 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), IEEE, 2024, pp. 1–6, https://doi.org/10.1109/CAMAD59638.2023.10478405.


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