Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782573
Title: Machine learning in optical fibre networking under uncertainty
Author: Meng, Fanchao
ISNI:       0000 0004 7968 1784
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2019
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Abstract:
During the past three decades, there have been major achievements on accurate modelling of behaviour and operation of telecommunication networks utilising classical methods with analytical or heuristic models. Currently, there is a big hype on the application of Artificial Intelligence (AI) and Machine Learning (ML) in the telecommunication space. However, there is a gap on scientific scrutiny of advantages of AI and ML compared to existing methods. Analogue telecommunication networks, i.e., optical and wireless networks seem to be the most suitable problem space for AI and ML. They are complicated network systems in nature that are highly dependent on ubiquitous physical layer uncertainties induced by subsystems and transmission mediums such as amplifiers, fibres, switches and transceivers. The problem space becomes even more complicated with recent advances in optical and wireless technologies that allow the development and operation of a fully programmable and dynamic network. This work focuses on benefits and applications of learning agents built on top of cognitive optical networks. It discusses the appropriateness of employing AI methods for specific problems in optical networks and address the importance of online learning with restricted monitoring data. It proposes and experimentally demonstrates a brand new way of carrying out optical network analytics utilising hybrid probabilistic and generative learning model which differs from traditional deterministic models. The result of this investigation, for the first time, can shed light into future AI and ML research in the optical network planning under uncertainty.
Supervisor: Simeonidou, Dimitra Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.782573  DOI: Not available
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