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Automated PIM Detection in Wireless Networks via Smart Data-Driven Techniques
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Authors: Gokcan Cantali, Eren Deniz, Hasan Ferit Eniser, Onur Yildirim, Gürkan Gür, Fatih Alagoz
Status: Final
Date of publication: 2 September 2024
Published in: ITU Journal on Future and Evolving Technologies, Volume 5 (2024), Issue 3, Pages: 407-421
Article DOI : https://doi.org/10.52953/KJIP1581
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Abstract:
As the number of base stations keeps increasing due to ever-growing wireless services and pervasive connectivity, the operational costs for network service providers rises proportionally. In addition, with the evolution of wireless technologies and the development of next generation networks such as 5G and 6G, network operators are inevitably expected to encounter more complex challenges. One such network issue is the Passive Intermodulation (PIM) problem that is observed in both 4G and 5G networks. It degrades the user experience and radio resource efficiency while leading to an operational overhead for detecting and mitigating it on the operator side. Although there is a significant body of work regarding PIM detection and cancellation methods, the majority of such studies depends on hardware solutions and manual investigation by network engineers, which is costly in terms of time and labor. In this paper, we propose two methods using unsupervised and semi-supervised Machine Learning (ML) approaches, namely a time-series-based anomaly detection technique and an autoencoder-based one, for identifying PIM problems in network sites. The proposed solutions utilize a set of Key Performance Indicator (KPI) data of base stations obtained from network management systems for a significantly long time interval and detect possible PIM problems without the need for a human in the loop. We measure and analyze the performance of our solutions, with the guidance of experienced network engineers, on our collected dataset. |
Keywords: Machine Learning (ML) for communications, Passive Intermodulation (PIM), Quality of Service (QoS), Radio Access Networks (RANs)
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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