Clustering Techniques for Profile Construction Using 5G Access Network Data

• Yves Dantas Neves UFRN
• Lázaro Raimundo de Oliveira UFRN
• João Carlos Xavier Júnior UFRN
• Anne M. P. Canuto UFRN

DOI: https://doi.org/10.5753/eniac.2024.245028

Abstract

The Internet and the development of Information and Communication Technologies have increased the volume and diversity of data sources, opening new opportunities in sectors for the application of Machine Learning and Big Data technologies. In this perspective, the Mobile Network Access infrastructure has been generating extensive amount of data. The main aim of this work is to apply clustering algorithms in order to identify profiles from data generated by 5G access network indicators regarding traffic, volume and channel quality. From the clustering partitions, we use the data profiles as dataset for a classification tool aiming to support fault identification, performance management and operational efficiency of access networks.

Keywords: Clustering Techniques, 5G Radio Access Networks, Performance Management

References

Aggarwal, C. and Reddy, C. (2018). Data Clustering: Algorithms and Applications. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. CRC Press.

Angadi, A. V., Yashaswini, S. D., Balaji, K., and Padma, U. (2019). Rf planning and optimization practices applied to improve the kpi’s of 4g lte network. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), pages 848–851.

Chen, Y., Liu, W., Niu, Z., Feng, Z., Hu, Q., and Jiang, T. (2020). Pervasive intelligent endogenous 6g wireless systems: Prospects, theories and key technologies. Digital Communications and Networks, 6(3):312–320.

Chiu, P., Reunanen, J., Luostari, R., and Holma, H. (2017). Big data analytics for 4.9g and 5g mobile network optimization. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pages 1–4.

Davies, D. L. and Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2):224–227.

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine learning research, 7:1–30.

Dunn, J. (2008). Well-separated clusters and optimal fuzzy partitions. Cybernetics and Systems, 4:95–104.

Everitt, B. S., Landau, S., and Leese, M. (2009). Cluster Analysis. Wiley Publishing, 4th edition.

Faceli, K., Lorena, A. C., Gama, J., Almeida, T. A. d., and Carvalho, A. C. P. d. L. F. d. (2021). Inteligência artificial: uma abordagem de aprendizado de máquina. LTC.

Guo, R. and Zhang, J. (2022). Research on 5g communication station location planning and regional clustering based on k-medoids and dbscan algorithm. In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), pages 1097–1101.

Hashmi, U. S., Darbandi, A., and Imran, A. (2017). Enabling proactive self-healing by data mining network failure logs. In 2017 International Conference on Computing, Networking and Communications (ICNC), pages 511–517.

Jun, L., Tingting, L., Gang, C., Hua, Y., and Zhenming, L. (2013). Mining and modelling the dynamic patterns of service providers in cellular data network based on big data analysis. China Communications, 10(12):25–36.

Liu, X., Chuai, G., Gao, W., Zhang, K., and Chen, X. (2019). Kqis-driven qoe anomaly detection and root cause analysis in cellular networks. In 2019 IEEE Globecom Workshops (GC Wkshps), pages 1–6.

Manzanilla-Salazar, O. G., Malandra, F., Mellah, H., Wetté, C., and Sansò, B. (2020). A machine learning framework for sleeping cell detection in a smart-city iot telecommunications infrastructure. IEEE Access, 8:61213–61225.

Margaris, A., Filippas, I., and Tsagkaris, K. (2022). Hybrid network–spatial clustering for optimizing 5g mobile networks. Applied Sciences, 12(3).

Parwez, M. S., Rawat, D. B., and Garuba, M. (2017). Big data analytics for user-activity analysis and user-anomaly detection in mobile wireless network. IEEE Transactions on Industrial Informatics, 13(4):2058–2065.

Raivio, K., Simula, O., Laiho, J., and Lehtimaki, P. (2003). Analysis of mobile radio access network using the self-organizing map. In IFIP/IEEE Eighth International Symposium on Integrated Network Management, 2003., pages 439–451.

Reed, S. (2012). Cognition: Theories and Applications. Cengage Learning.

Rekkas, V. P., Sotiroudis, S., Sarigiannidis, P., Karagiannidis, G. K., and Goudos, S. K. (2021). Unsupervised machine learning in 6g networks-state-of-the-art and future trends. In 2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST), pages 1–4.

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.

Santos, R., Sousa, M., Vieira, P., Queluz, M. P., and Rodrigues, A. (2019). An unsupervised learning approach for performance and configuration optimization of 4g networks. In 2019 IEEE Wireless Communications and Networking Conference (WCNC), pages 1–6.

Srinivasan, M., Skaperas, S., Mitev, M., Herfeh, M. S., Shehzad, M. K., Sehier, P., and Chorti, A. (2023). Smart channel state information pre-processing for authentication and symmetric key distillation. IEEE Transactions on Machine Learning in Communications and Networking, 1:328–345.

Wang, S. and Ferrús, R. (2021). Extracting cell patterns from high-dimensional radio network performance datasets using self-organizing maps and k-means clustering. IEEE Access, 9:42045–42058.

Xu, F., Li, Y., Wang, H., Zhang, P., and Jin, D. (2017). Understanding mobile traffic patterns of large scale cellular towers in urban environment. IEEE/ACM Transactions on Networking, 25(2):1147–1161.

Zhu, Q. and Sun, L. (2020). Big data driven anomaly detection for cellular networks. IEEE Access, 8:31398–31408.