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    Telemetry and Data Collection for Artificial Intelligence in Optical Systems

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    Author
    Biswas, Aishik
    Issue Date
    2020
    Keywords
    Artificial Intelligence
    Data Collection
    Machine Learning
    Optical Communications
    Optical Systems
    Telemetry
    Advisor
    Kilper, Daniel C.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Increasing internet traffic, along with rising complexity of optical communication systems have motivated the development of novel experimental, mathematical and computational tools. Optical networks are being used in more applications such as 5G wireless systems in which internet traffic continuously varies in both time and location. Slow reconfiguration in optical networks leads to network inefficiency and under-utilization of resources. This problem created interest in realizing a reliable method that can perform real-time switching and wavelength provisioning, in order to adapt to the fluctuating traffic patterns. Such systems having rapid-switching capabilities are susceptible to power excursions that diminish the Quality of Transmission (QoT). Widely used reconfigurable optical add-drop multiplexer (ROADM) based transparent optical networks, which do not need optical-electrical-optical (O-E-O) conversion, can potentially benefit from signal quality monitoring in the optical domain (i.e. optical performance monitoring). Additionally, for optical switching the QoT must be evaluated prior to establishing the new wavelength route in a given transparent wavelength division multiplexed (WDM) network. The complexity of optical communication systems has resulted in researchers looking for new methods to determine QoT. Artificial Intelligence (AI) and its sub-field of Machine Learning (ML) together with telemetry to collect more data have attracted interest for use in optical networks to improve QoT prediction. In this thesis, a non-disruptive optical probe based real-time telemetry solution capable of measuring QoT and performing optical performance monitoring (OPM) was built. Our device, the non-disruptive optical probe monitor (ND-OPM) uses short optical pulses to probe a network’s performance to obtain data for QoT metrics. Unlike other methods, probing can be done before wavelength provisioning/reconfiguration, supporting wavelength routing and assignment decisions prior to switching. Using this technique, the wavelength dependent gain of an Erbium-doped fiber amplifier (EDFA) was estimated non-disruptively, along with prediction of the channel power and optical signal to noise ratio (OSNR) of a provisioned channel. Additionally, a remote and automated SDN-based process for running experiments and collecting large datasets required to characterize optical systems deployed in the COSMOS Testbed was studied. The experimental results include measurements of amplifier gain and power spectra for randomized channel loading configurations. This data can be used to develop ML algorithms for QoT estimation. Finally, we have reviewed some of the AI/ML techniques that have been applied to the field of optical communications.
    Type
    text
    Electronic Thesis
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Optical Sciences
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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