Abstract
Any mobile network operator’s primary concern is ensuring a better customer experience for their subscribers. For this reason, they need to ensure that their infrastructure is working correctly. However, managing telecommunication infrastructure, especially cellular base stations, has never been an obvious task in the African and Middle Eastern regions due to the landlocked nature and lack of access roads, especially in rural areas. Despite the many solutions developed by operators, ranging from monitoring tools to the deployment of technicians in the field, this still needs to be solved. Some operators prefer to entrust these cell sites to Managed Service Providers (MSPs) or Tower Companies (TowerCos) and concentrate on other services. To address this issue, we propose an adapted correlation clustering for cell site management, considering the operator’s parameters and a site accessibility parameter. This approach makes it possible to determine the optimal number of cells to allocate to a technician to make his interventions efficient; this will minimize Operational Expenditure (OpEx) and cell downtime due to breakdowns and maximize the quality of service offered to customers.
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References
Angell, R., Monath, N., Yadav, N., McCallum, A.: Interactive correlation clustering with existential cluster constraints. In: International Conference on Machine Learning, pp. 703–716. PMLR (2022)
Ari, A.A.A., Djedouboum, A.C., Gueroui, M., Thiare, O., Mohamadou, A., Aliouat, Z.: A three-tier architecture of large-scale wireless sensor networks for big data collection. Appl. Sci. 10(15), 5382 (2020)
Ari, A.A.A., Gueroui, A., Titouna, C., Thiare, O., Aliouat, Z.: Resource allocation scheme for 5G C-RAN: a swarm intelligence based approach. Comput. Netw. 165, 106957 (2019)
Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Machine learning 56(1), 89–113 (2004)
BearingPoint: Electricite et telecom en afrique: la convergence? (2017). https://www.agenceecofin.com/. Accessed 18 Sept 2021
Brickner, T.: Closing Africa’s infrastructure gap with sustainability at the heart of Helios towers. shorturl.at/dkMS2 (2020). Accessed 12 May 2021
Chierichetti, F., Dalvi, N., Kumar, R.: Correlation clustering in mapreduce. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 641–650 (2014)
Demaine, E.D., Emanuel, D., Fiat, A., Immorlica, N.: Correlation clustering in general weighted graphs. Theoret. Comput. Sci. 361(2), 172–187 (2006). https://doi.org/10.1016/j.tcs.2006.05.008. https://www.sciencedirect.com/science/article/pii/S0304397506003227, approximation and Online Algorithms
Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self-organizing map. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 6, pp. 15–19 (2000). https://doi.org/10.1109/IJCNN.2000.859366
Djedouboum, A.C., Ari, A.A.A., Gueroui, A.M., Mohamadou, A., Thiare, O., Aliouat, Z.: A framework of modeling large-scale wireless sensor networks for big data collection. Symmetry 12(7), 1113 (2020)
Gbadoubissa, J.E.Z., Ari, A.A.A., Gueroui, A.M.: Efficient k-means based clustering scheme for mobile networks cell sites management. J. King Saud Univ. Comput. Inf. Sci. 32(9), 1063–1070 (2020). https://doi.org/10.1016/j.jksuci.2018.10.015. https://www.sciencedirect.com/science/article/pii/S131915781830778X
Gbadouissa, J.E.Z., Ari, A.A.A., Titouna, C., Gueroui, A.M., Thiare, O.: HGC: hypergraph based clustering scheme for power aware wireless sensor networks. Future Gener. Comput. Syst. 105, 175–183 (2020). https://doi.org/10.1016/j.future.2019.11.043. https://www.sciencedirect.com/science/article/pii/S0167739X1932240X
GSMA: GSMA Connected Society, Closing the Coverage Gap. GSM Association (2019)
GSMA: The mobile economy 2020. GSM Association, 1 edn. (2020)
Hongsakham, W., Pattara-atikom, W., Peachavanish, R.: Estimating road traffic congestion from cellular handoff information using cell-based neural networks and k-means clustering. In: 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, vol. 1, pp. 13–16 (2008). https://doi.org/10.1109/ECTICON.2008.4600361
Hung, C., Tsai, C.F.: Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Syst. Appl. 34, 780–787 (2008). https://doi.org/10.1016/j.eswa.2006.10.012
OpenCelliD: What is opencellid? https://opencellid.org/#zoom=16&lat=37.77889&lon=-122.41942. Accessed 20 June 2021
Ray, S., Turi, R.: Determination of number of clusters in k-means clustering and application in colour image segmentation. In: Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques (ICAPRDT 1999) 1, August 2000
Satista: Number of active mobile broadband subscriptions worldwide from 2007 to 2021 (2021). https://www.statista.com/statistics/273016/number-of-mobile-broadband-subscriptions-worldwide-since-2007/. Accessed 28 Aug 2022
WFPGeoNode: Metadata: Cameroon road network (main roads). https://geonode.wfp.org/layers/geonode:cmr_trs_roads_osm/metadata_detail. Accessed 04 Apr 2021
Yang, G., Esmailpour, A., Nasser, N., Chen, G., Liu, Q., Bai, P.: A hierarchical clustering algorithm for interference management in ultra-dense small cell networks. IEEE Access PP, 1 (2020). https://doi.org/10.1109/ACCESS.2020.2989502
Zimmermann, H.m., Seitz, A., Halfmann, R.: Dynamic cell clustering in cellular multi-hop networks. In: 2006 10th IEEE Singapore International Conference on Communication Systems, pp. 1–5 (2006). https://doi.org/10.1109/ICCS.2006.301458
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We thank the editor and the anonymous reviewers for their valuable remarks that helped us improve the paper’s content and presentation. Moreover, the author is grateful for the facilities the AIMS-Cameroon Research Center provides and its kind hospitality.
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Abba Ari, A.A. et al. (2024). Correlation Clustering Adapted for Cell Site Management of Mobile Networks in Developing Countries. In: Tchakounte, F., Atemkeng, M., Rajagopalan, R.P. (eds) Safe, Secure, Ethical, Responsible Technologies and Emerging Applications. SAFER-TEA 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-031-56396-6_8
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