[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116893-en":3,"doc-seo-116893-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116893,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning for Optical Fibre Communication Systems - Doctor of Philosophy Thesis","Global internet traffic growth strains optical fibre communication systems, creating a risk of capacity crunch that threatens modern network performance. This thesis targets increased capacity by leveraging machine learning, motivated by the data volume from such systems, operational complexity under nonlinear effects, and machine learning’s proven success in applied domains, including optical networks. It evaluates key desired properties: use of prior knowledge, interpretable outputs, well-quantified predictive uncertainty, and transparent model design.","Machine Learning for Optical Fibre Communication Systems  \nJoshua Nevin  \nDepartment of Engineering  \nUniversity of Cambridge  \nThis thesis is submitted for the degree of Doctor of Philosophy  \nGonville and Caius College September 2022  \nDeclaration  \nThis thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. I further state that no substantial part of my thesis has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. It does not exceed the prescribed word limit for the relevant Degree Committee.  \nJoshua Nevin  \nSeptember 2022  \nAbstract  \nMachine Learning for Optical Fibre Communication Systems  \nJoshua Nevin  \nGlobal demand for internet traffic is growing at a rapid rate, driven by the adoption of new technologies and increased demand from consumers. This continued growth is exerting pressure on optical fibre communication systems and networks, which carry the bulk of modern internet traffic, leading to a risk of a capacity crunch. The overarching goal of this thesis is to alleviate this pressure by increasing the capacity of optical fibre communication systems. Machine learning is an attractive technology to help achieve this goal, due to the vast quantities of data generated by these systems, the complexity of their operation in the face of nonlinearities and the demonstrated success of machine learning in a vast array of applied fields, including optical networks. We highlight a number of desirable properties for machine learning methods applied within the optical fibre communications domain, namely the effective use of a priori knowledge, interpretable model outputs, wellquantified predictive uncertainty and transparent model design, and discuss to what extent these properties are satisfied by the work in this thesis. First, we focus on estimation of the physical layer parameters at the receiver, to increase the capacity by reducing the uncertainty associated with the physical layer in a non-disruptive way. Gaussian process regression is leveraged to learn a probabilistic, computationally cheap mapping from the physical layer parameters to SNR. This is applied within both a novel history matching-based parameter estimation technique and a novel approach for optimisation of the physical layer parameters in terms of the gain afforded by digital backpropagation. We then consider how to embed our a priori knowledge within the machine learning model itself, for both link and network scale problems. A novel technique is presented based on multi-task learning for combining a priori information from physical models of transmission with measured data from and experimental optical fibre communication link within the framework of a Gaussian process. Then, we show how a priori knowledge can be used to increase the efficacy of machine learning for network scale problems, embedding information describing the current network state into the action space of a reinforcement learning solution for routing and wavelength assignment in a simulated optical network. Planned future work will focus on extending the presented techniques to better incorporate the desirable machine learning properties outlined and increase the scope of applicability to more complex systems.  \nAcknowledgements  \nAnd I would like to thank my supervisor Seb Savory for his continued support, knowledge and attention to detail. I would also like to thank my co-authors from the publications arising from this PhD, namely Fancisco Javier Vaquero-Caballero, David Ives, Nikita Shevchenko, Sam Nallaperuma, Zak Shabka, Georgios Zervas, Eric Sillekens, Ronit Sohanpal, Lidia Galdino and Polina Bayvel, for their insight, hard work and stimulating discussion. I also thank th","cbCair81RXGooSL0","https://ap.wps.com/l/cbCair81RXGooSL0","pdf",7305569,1,184,"English","en",105,"# Abstract\n## Motivation and thesis goal\n## Desired properties for machine learning methods\n## Physical-layer parameter estimation\n## Embedding prior knowledge in models\n## Link- and network-scale applications\n## Planned future work","[{\"question\":\"Why does the thesis focus on increasing capacity in optical fibre communication systems?\",\"answer\":\"Rising global internet traffic pressures optical fibre networks, creating a risk of a capacity crunch and reduced ability to carry modern demand.\"},{\"question\":\"What role does machine learning play in the proposed approach?\",\"answer\":\"Machine learning is used to exploit large data volumes and handle nonlinear operational complexity, aiming to improve capacity through lower uncertainty and better optimization.\"},{\"question\":\"Which machine learning methods and ideas are highlighted?\",\"answer\":\"Gaussian process regression is used for probabilistic mapping to SNR, alongside history matching-based parameter estimation and reinforcement learning for routing and wavelength assignment, with prior knowledge embedded into models.\"}]","Machine Learning for Optical Fibre Communication Systems - 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