Machine learning for digital twins of optical networking infrastructure

光网络基础设施数字孪生的机器学习

• 批准号: 2634875
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2634875
• 关键词: Machine; learning; digital; twins; optical

In core networks there is a huge quantity of metrology available ranging from SNR and BER measured by the transceivers through to the input and output powers of optical amplifiers and switches. The aim of this PhD project would be to use machine learning applied to metrology information, combined with the underpinning physics to create a digital twin of the physical optical network infrastructure. Not only would the digital twin be updated in real-time, but it would also incorporate the uncertainty into the model allowing for real-time margin allocation to improve the operation of the optical network. Ultimately it would allow the optical network to operate at the limits of performance, based on measurements of the actual infrastructure, so as to allow the throughput as a function of availability to be optimised, to give the best user experience of a given installed infrastructure.Key challenges the student will need to consider include:1. The choice of the models for the digital twins (including whether it is a statistical model rather than purely a deterministic physics-based model)2. The optimal means of incorporating measurement uncertainty into the models to create the digital twin3. How to use the digital twin to give information regarding throughput, availability etc. so as to optimise the overall performance of the optical network4. Ultimately, how to change current practice regarding the design and operation of the high availabilityoptical networks based on a data driven, probabilistic based approach, rather than the established margin-based approach.The research strategy would start with a single element, such as an optical amplifier, transmitter or receiver and create a mathematical model that is refined based on measurements taken. Research currently carried out in Cambridge has demonstrated that using Gaussian Processes with priors based on a physical model allows for a physically informed machine learning approach. While it is anticipated a Bayesian framework would be utilised, options of physical models and shades of grey, ranging from white box models to black box models would be explored. Having created the mathematical basis for a digital twin the statistical variation in the performance can be quantified and verified, with the complexity of the system increased until initially a digital twin is created for the 1200 km link in the Cambridge lab.The application of the digital twin to optical infrastructure is a concept that is only just emerging.Within the Cambridge University Engineering Department work is already underway on the National Digital Twin Programme https://www.cdbb.cam.ac.uk/what-we-do/national-digital-twin-programme, which we would hope to leverage as part of the same department as the PhD student and the PI.While the project would create a methodology and software to enable a digital twin of the optical infrastructure to be formulated, ultimately the aim of the project would be to create a paradigm shiftin the design and operation of the high-availability optical networks.

在核心网络中，从收发器测量的信噪比和误码率到光放大器和开关的输入和输出功率，都有大量可用的计量方法。这个博士项目的目的是将机器学习应用于计量信息，结合基础物理，创建物理光网络基础设施的数字孪生。数字孪生不仅可以实时更新，而且还可以将不确定性纳入模型，以便实时分配余量，以改善光网络的运行。最终，它将允许光网络在性能极限下运行，基于对实际基础设施的测量，从而允许吞吐量作为可用性的函数得到优化，从而为给定安装的基础设施提供最佳的用户体验。学生需要考虑的主要挑战包括：1。数字孪生模型的选择（包括它是一个统计模型，还是一个纯粹的基于确定性物理的模型）将测量不确定度纳入模型以创建数字孪生的最佳方法3。如何利用数字孪生来提供吞吐量、可用性等信息，从而优化光网络的整体性能。最后，如何改变目前基于数据驱动、基于概率的方法而不是基于既定边际的方法的高可用性光网络设计和运行的实践。研究策略将从单个元件开始，如光学放大器、发射器或接收器，并根据所进行的测量创建一个数学模型。目前在剑桥进行的研究表明，使用基于物理模型的高斯过程可以实现物理信息的机器学习方法。虽然预计将使用贝叶斯框架，但将探索物理模型和灰色阴影的选择，从白盒模型到黑盒模型。在创建了数字孪生的数学基础之后，性能上的统计变化可以被量化和验证，随着系统复杂性的增加，直到最初在剑桥实验室为1200公里的链路创建了数字孪生。数字孪生在光学基础设施中的应用是一个刚刚出现的概念。在剑桥大学工程系，国家数字双胞胎计划https://www.cdbb.cam.ac.uk/what-we-do/national-digital-twin-programme的工作已经在进行中，我们希望利用它作为博士生和PI的同一部门的一部分。虽然该项目将创建一种方法和软件，以使光学基础设施的数字孪生得以制定，但该项目的最终目标是在高可用性光网络的设计和操作中创建一种范式转变。