Abstract
The application of Artificial Intelligence (AI)-based knowledge discovery mechanisms for supporting the automation of wireless network operations is envisaged to fertilize in future Fifth Generation (5G) systems due to the stringent requirements of these systems and to the advent of big data analytics. This paper intends to elaborate on the demonstration of knowledge discovery capabilities in the context of the architecture proposed by the Small cEllS coordinAtion for Multi-tenancy and Edge services (SESAME) project that deals with multi-operator cloud-enabled small cells. Specifically, the paper presents the considered demonstration framework and particularizes it for supporting an energy saving functionality through the classification of cells depending on whether they can be switched off during certain times. The framework is illustrated with some results obtained from real small cell deployments.
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Acknowledgements
This work has been supported by the EU funded H2020 5G-PPP project SESAME under the grant agreement 671596 and by the Spanish Research Council and FEDER funds under RAMSES grant (ref. TEC2013-41698-R).
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Pérez-Romero, J., Sánchez-González, J., Sallent, O., Whitehead, A. (2017). On Introducing Knowledge Discovery Capabilities in Cloud-Enabled Small Cells. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_57
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DOI: https://doi.org/10.1007/978-3-319-65172-9_57
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