Clustering Techniques for Profile Construction Using 5G Access Network Data
Resumo
A Internet e o desenvolvimento das Tecnologias da Informação e Comunicação expandiram o volume e a diversidade das fontes de dados, abrindo assim novas oportunidades nos setores industriais e acadêmicos à aplicações de tecnologias relacionadas ao Aprendizado de Máquina (AM) e Big Data. Nessa perspectiva, a infraestrutura de Acesso das Redes Móveis vem gerando uma grande quantidade de dados. O presente trabalho tem como objetivo principal a aplicação de algoritmos de agrupamento para a criação de perfis a partir de dados relacionados à indicadores de redes de acesso 5G referentes a tráfego, volume e qualidade de canal. A partir das partições geradas na etapa de agrupamento, espera-se que os perfis encontrados possam servir de base para uma ferramenta de suporte a identificação de falhas, gestão de desempenho e eficiência operacional das redes de acesso.
Palavras-chave:
Técnicas de Agrupamento, Redes de Acesso 5G, Gestão de Desempenho
Referências
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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.
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Zhu, Q. and Sun, L. (2020). Big data driven anomaly detection for cellular networks. IEEE Access, 8:31398–31408.
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.
Publicado
17/11/2024
Como Citar
NEVES, Yves Dantas; OLIVEIRA, Lázaro Raimundo de; XAVIER JÚNIOR, João Carlos; CANUTO, Anne M. P..
Clustering Techniques for Profile Construction Using 5G Access Network Data. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 251-262.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245028.
