Abstract
The adoption of Software Define Networking (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) will play a key role in the control and management of 5G network slices to fulfill the specific requirements of application/services and the new requirements of fifth generation (5G) networks. In this research, we propose a distributed architecture to perform network analytics applying ML techniques in the context of network operation and control of 5G networks.
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Hernández-Chulde, C., Cervelló-Pastor, C. (2019). Intelligent Optimization and Machine Learning for 5G Network Control and Management. In: De La Prieta, F., et al. Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection. PAAMS 2019. Communications in Computer and Information Science, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-24299-2_33
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DOI: https://doi.org/10.1007/978-3-030-24299-2_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24298-5
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