机器学习技术在光网络中的应用综述
An Overview on Application of Machine Learning Techniques in Optical Networks
摘要：
当今的电信网络已成为大量广泛异构数据的来源。这些信息可以从网络流量跟踪、网络警报、信号质量指标、用户行为数据等中检索。需要高级数学工具从这些数据中提取有意义的信息，并从网络生成的数据中做出与网络正常运行有关的决策。在这些数学工具中，机器学习（ML）被认为是执行网络数据分析并实现自动化网络自配置和故障管理的最有前途的方法之一。ML技术在光通信网络领域的采用是由过去几年中光网络所面临的网络复杂性的前所未有的增长所推动的。这种复杂性的增加是由于引入了大量的可调节的和相互依赖的系统参数（例如，路由配置、调制格式、符号率、编码方案等）其通过使用相干发射/接收技术、先进的数字信号处理和光纤传播中的非线性效应的补偿而实现。在本文中，我们提供了一个概述ML光通信和网络的应用。我们对涉及该主题的相关文献进行了分类和调查，并为对该领域感兴趣的研究人员和从业者提供了ML的入门教程。虽然最近出现了大量的研究论文，ML光网络的应用仍处于起步阶段：为了刺激在这一领域的进一步工作，我们得出结论，提出新的可能的研究方向。
英文摘要：
Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
