Machine Learning Enhanced Quality of Transmission Estimation in Disaggregated Optical Systems
PublisherThe University of Arizona.
RightsCopyright © 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.
AbstractTelecommunication systems have been through continuous evolution to keep up the fast-growing network traffic demand. With developments such as HD video streaming, cloud computing, Internet of Things (IoT), hyper-scale data centers, and 5G wireless networks, more demanding network requirements raise challenges to create a more efficient optical communication system that can provide the capability to support a wide range of applications. Specifically, 5G standards require more bandwidth and ultra-low latency; metro-scale optical aggregation networks motivate more scalable and on-demand optical network capacity. Dynamic reconfigurable optical add-drop multiplexer (ROADM) based wavelength-division multiplexing (WDM) systems in which connections are established through real-time wavelength switching operations have long been studied as a means of achieving greater scalability and increasing the network resource utilization. A new dimension referred to as disaggregated optical systems, has the potential to further drive down cost by commoditizing the hardware. While ROADMs are extensively deployed in today’s WDM systems, their interoperability and functionality remain limited. Recent advances in hardware and software such as optical physical layer software-defined networking (SDN) significantly improve the multi-layer control and management potential of ROADM systems even facilitating wavelength switching. However, ensuring stable performance and reliable quality of transmission (QoT) remain severe problems, particularly for disaggregated systems. A key challenge in enabling disaggregated optical systems is the uncertainty and optical power dynamics that arise from a variety of physical effects in the amplifiers and transmission fiber. This thesis examines the potential for machine learning for QoT estimation in software defined networking control, and its application to disaggregated ROADM systems. Current physical layer control of flexible meshed optical networks with dynamic reconfigurability is reviewed, and future network control plane architectures based on disaggregated optical systems are discussed. To enable high capacity and low latency in inter-domain routing, a transparent software defined exchange (tSDX) is proposed and implemented. To serve a broadening range of applications and increase network efficiency, a novel transmission system based on hysteresis controlled adaptive coding is studied, which can adapt to diverse and changing transmission conditions, including optical signal-to-noise (OSNR) variations. To resolve optical channel power excursions caused by wavelength operation in optically amplified networks, the dual laser switching technique is proposed and experimentally verified, which is able to cancel out the excursion. To build an accurate numerical model for an optical amplifier, which is a critical component in the calculation of the QoT, a novel machine learning (ML) model is studied based on deep neural networks (DNN) and supervised learning. Experimental results demonstrate the superiority of ML-based modeling in prediction accuracy of the optical channel power and gain spectrum of Erbium-Doped Fiber Amplifiers (EDFA). A hybrid machine learning (HML) model, which combines a-priori knowledge (the empirical numerical model) and a-posteriori knowledge (supervised machine learning model) is proposed and realized, which is shown to reduce the training complexity, both in time and space, compared to an analytical or neural network-based model. The potential improvement to the current QoT estimation framework is proposed and analyzed, based on this enhanced EDFA model.
Degree ProgramGraduate College
Electrical & Computer Engineering