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Prediction of low accessibility in 4G networks

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Abstract

The increased programmability of communication networks makes them more autonomous, and with the ability to actuate fast in response to users and networks’ events. However, it is usually a difficult task to understand the root cause of the network problems, so that autonomous actuation can be provided in advance. This paper analyzes the probable root causes of reduced accessibility in 4G networks, taking into account the information of important key performance indicators (KPIs), and considering their evolution in previous time-frames. This approach resorts to interpretable machine learning models to measure the importance of each KPI in the decrease of the network accessibility in a posterior time-frame. The results show that the main root causes of reduced accessibility in the network are related with the number of failure handovers, the number of phone calls and text messages in the network, the overall download volume, and the availability of the cells. However, the main causes of reduced accessibility in each cell are more related to the number of users in each cell and its download volume produced. The results also show the number of principal component analysis (PCA) components required for a good prediction, as well as the best machine learning approach for this specific use case. In addition, we finished our considerations with a discussion about 5G network requirements where proactivity is mandatory.

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Notes

  1. https://www.statisticssolutions.com/what-is-logistic-regression/

  2. https://towardsdatascience.com/an-intuitive-explanation-of-random-forest-and-extra-trees-classifiers-8507ac21d54b

  3. https://towardsdatascience.com/understanding-gradient-boosting-machines-9be756fe76ab

  4. https://towardsdatascience.com/understanding-adaboost-2f94f22d5bfe

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  7. https://victorzhou.com/blog/gini-impurity/

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  9. https://www.3gpp.org/

  10. https://5go.pt

  11. https://www.onap.org/

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Funding

This work is supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Lisbon (POR LISBOA 2020) and the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 framework (Project 5G with Nr. 024539 (POCI-01-0247-FEDER-024539)), and by FCT/MEC through national funds and when applicable co-funded by FEDER—PT2020 partnership agreement under the project UID/EEA/50008/2019.

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Authors and Affiliations

  1. Instituto de Telecomunicações, Aveiro, Portugal

    Carlos Senna

  2. DETI, University of Aveiro and Instituto de Telecomunicações, Aveiro, Portugal

    Diogo Ferreira, Paulo Salvador & Susana Sargento

  3. Altice Labs, Aveiro, Portugal

    Luís Cortesão, Cristina Pires & Rui Pedro

Authors
  1. Diogo Ferreira
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  2. Carlos Senna
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  3. Paulo Salvador
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  4. Luís Cortesão
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  5. Cristina Pires
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  6. Rui Pedro
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  7. Susana Sargento
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Corresponding author

Correspondence to Carlos Senna.

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Ferreira, D., Senna, C., Salvador, P. et al. Prediction of low accessibility in 4G networks. Ann. Telecommun. 77, 421–435 (2022). https://doi.org/10.1007/s12243-021-00849-9

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