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Ali-Tolppa, J., Kocsis, S., Schultz, B., Bodrog, L., & Kajo, M. 2018, Self-healing and resilience in future 5g cognitive autonomous networks. Paper presented at 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K). 
Added by: SijanLibrarian (2021-09-07 09:00:35)   Last edited by: SijanLibrarian (2021-09-07 09:03:04)
Resource type: Proceedings Article
BibTeX citation key: AliTolppa2018
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Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General, Military Science
Subcategories: 5G, Autonomous systems, Big data, Cloud computing, Decision making, Deep learning, Edge AI, Internet of things, Machine intelligence, Machine learning, Networked forces, Neural nets, Q-learning, Systems theory
Creators: Ali-Tolppa, Bodrog, Kajo, Kocsis, Schultz
Collection: 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K)
Views: 37/43
Views index: 19%
Popularity index: 4.75%
In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect, diagnose and correct degraded states in the managed network functions or other resources. Such methods are increasingly important in future network deployments, since ultra-high reliability is one of the key requirements for the future 5G mobile networks, e.g. in critical machine-type communication. In this paper, we discuss the considerations for improving the resiliency of future cognitive autonomous mobile networks. In particular, we present an automated anomaly detection and diagnosis function for SON self-healing based on multi-dimensional statistical methods, case-based reasoning and active learning techniques. Insights from both the human expert and sophisticated machine learning methods are combined in an iterative way. Additionally, we present how a more holistic view on mobile network self-healing can improve its performance.
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