Smart Optical Networks Enabled by Machine Learning

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The continuous traffic increase has led to wavelength division multiplexing (WDM) transmission systems with ever-increasing data rates, capacity and flexibility. Video and cloud applications, as well as future 5G and Internet of Things (IoT), call for even higher traffic volumes and dynamicity in optical networks. This makes the potential impact of performance degradation and failure at the link and network levels more severe and the need for flexible and autonomous network management more important than ever. Machine learning (ML) has been researched in the last ten years as a way to bring Artificial intelligence (AI) in wireless networks for optimizing spectrum and network resources, but its application in optical networks is still in its infancy. The availability of flexible transponders with monitoring capabilities makes it possible to leverage the potential of ML to design and manage increasingly heterogeneous, dynamic and complex networks in a software-defined network (SDN) context.*******As we are entering into the era of knowledge-defined networking (KDN), the proposed research program aims at investigating cognitive concepts for improving the flexibility and robustness of optical networks through adaptive and learning mechanisms. The program will explore ML-based methods for QoT estimation and optical signal to noise ratio (OSNR) prediction in the optical layer, as well as for anomaly detection and proactive fault management at the network level. Supervised and unsupervised ML, as well as reinforcement learning (RL) techniques, will be investigated using both synthetic and field data. The long-term objective of the proposed research program is to perform a testbed demonstration of a KDN showing the benefits of ML for performance prediction and the advantages of proactive over conventional reactive approaches for lightpath provisioning and fault management.*******The originality and scientific impact of the proposed research program lies in the huge potential of ML to be exploited in the field of optical networking, which opens extremely rich opportunities for innovation, as well as on the availability of field data for training the ML models and making them more suitable for real-world applications. The results of this research program will enable the development of preventive strategies for making high capacity optical networks more reliable and easier to manage. Some additional innovations include: new ML models for QoT estimation; new predictive methods for performance evolution and anomaly detection; new proactive schemes for simplified and automated network operation and higher network reliability. The potential for training highly qualified personnel (HQP) involving multidisciplinary research on advanced networking and ML is a major asset of the research program. Three Ph.D. students, four Master's students and several undergraduate students will be involved in the research program to be completed in an expected time frame of five years.******