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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Deep Learning Based Optical Performance Monitoring for Digital Coherent Optical Receivers
Committee:
Dr. Stephen Ralph, ECE, Chair , Advisor
Dr. Benjamin Klein, ECE
Dr. Justin Romberg, ECE
Dr. Sorin Tibuleac, Adva Optical Networking
Dr. Jhon James Granada Torres, Universidad de Antioquia in Columbia
Abstract: Optical performance monitoring techniques are required to ensure reliable transmission in all types of optical systems. Optical performance monitoring techniques facilitate the estimation of link-degrading impairments such as optical signal-to-noise ratio degradations and nonlinear intrusions that are difficult to assess using conventional measurement methods. The development of new optical performance monitoring techniques will aid in the deployment of new links and monitoring of deployed networks. The objectives of this research are (a) to develop machine learning techniques that can estimate optical performance monitoring metrics in optical communication when deploying a new optical link and assess the condition of established links; (b) to assess the performance of the associated machine learning techniques; (c) to understand the factors that limit performance estimation; and (d) to identify optimal proxies for applying machine learning in digital coherent optical receivers.
