Smart Optical Networks Enabled by Machine Learning

机器学习支持的智能光网络

• 批准号: RGPIN-2019-03972
• 负责人: Tremblay, Christine
• 金额: $ 3.35万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737311
• 关键词: Smart; Optical; Networks; Enabled; Machine

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.

业务量的持续增长导致波分复用（WDM）传输系统具有不断增长的数据速率、容量和灵活性。视频和云应用以及未来的5G和物联网（IoT）要求光网络具有更高的流量和动态性。这使得链路和网络级别的性能下降和故障的潜在影响更加严重，对灵活和自主网络管理的需求比以往任何时候都更加重要。机器学习（ML）在过去十年中一直被研究作为将人工智能（AI）引入无线网络以优化频谱和网络资源的一种方式，但其在光网络中的应用仍处于起步阶段。具有监控功能的灵活转发器的可用性使得可以利用ML的潜力来设计和管理软件定义网络（SDN）环境中日益异构，动态和复杂的网络。 随着我们进入知识定义网络（KDN）时代，拟议的研究计划旨在调查认知概念，通过自适应和学习机制提高光网络的灵活性和鲁棒性。该计划将探索基于ML的方法，用于光学层的QoT估计和光信噪比（OSNR）预测，以及网络级的异常检测和主动故障管理。监督和无监督ML以及强化学习（RL）技术将使用合成和现场数据进行研究。拟议研究计划的长期目标是进行KDN的测试台演示，展示ML在性能预测方面的优势，以及主动式方法相对于传统反应式方法在光路配置和故障管理方面的优势。 拟议研究计划的原创性和科学影响在于ML在光网络领域的巨大潜力，这为创新提供了极其丰富的机会，以及现场数据的可用性，用于训练ML模型并使其更适合现实世界的应用。这项研究计划的结果将有助于制定预防策略，使高容量光网络更可靠，更易于管理。一些其他创新包括：用于QoT估计的新ML模型;用于性能演进和异常检测的新预测方法;用于简化和自动化网络操作以及更高网络可靠性的新主动方案。培养高素质人才（HQP）的潜力涉及先进网络和ML的多学科研究是研究计划的主要资产。三个博士学生，四名硕士生和几名本科生将参与研究计划，将在五年的预期时间内完成。