基于机器学习的光网络监测与优化方法
Optical Network Monitoring and Optimization Methods based on Machine Learning
李鸿 1刘武 2罗鸣2
作者信息
- 1. 中国信息通信科技集团有限公司 武汉邮电科学研究院有限公司,武汉 430074
- 2. 中国信息通信科技集团有限公司 光通信技术和网络全国重点实验室,武汉 430074
- 折叠
摘要
近年来,许多新型调制和复用技术以及动态网络概念被提出,以适应不断提高的网络带宽和质量需求.网络控制平台向系统化、智能化趋势发展,要求网络管理者不断监测网络各项参数,时刻优化网络状态.然而,大范围部署额外的监测设备获取参数信息从成本控制的角度缺乏可行性,利用已知数据与特殊算法进行网络性能监测和优化是更优的选择.机器学习方法因为其足够准确和高效逐渐被学术界采纳用于完成上述任务.文章梳理了在光网络监测与优化任务中使用机器学习算法的不同应用场景,综述了该领域的研究成果,并提出了现存的基于机器学习的光网络监测与优化方法存在的问题及可能的进一步研究的方向.基于机器学习的光学性能监测包括光学损伤辨别、信道质量评估以及通道功率预测,基于机器学习的网络配置优化方法包括强化学习优化通道功率.进一步研究方向可以考虑加强与运营商的合作,使用真实的现场数据,不断获取数据动态训练模型,并使用迁移学习和数据增强等技术,以保证算法的鲁棒性与泛化能力.
Abstract
In recent years,many new modulation and multiplexing technologies and dynamic network concepts have been proposed to adapt to the ever-increasing network bandwidth and quality requirements.Network control platform has a systematic and intelli-gent development trend,which requires network managers to constantly monitor the parameters of the network and optimize the network state.However,it is not feasible to arrange additional monitoring equipment in a large range to obtain parameter informa-tion from the perspective of cost control.It is better to use known data and special algorithms to monitor and optimize network per-formance.Machine learning methods are increasingly adopted by the academic community because they are accurate and efficient enough to accomplish these tasks.This paper first reviews the different application scenarios of machine learning algorithms in op-tical network monitoring and optimization tasks.Then it reviews the research achievements in this field,and puts forward the ex-isting problems of machine learning-based optical network monitoring and optimization methods as well as the possible direction of future research.The optical performance monitoring based on machine learning includes failure identification,quality of transmis-sion estimation and channel power prediction.The network configuration optimization method based on machine learning includes reinforcement learning to optimize channel power.For future research direction,we believe that it is possible for researchers to use real data from network operators,newly collected data to dynamically train the model,and transfer learning and data enhancement techniques to ensure the robustness and generalization ability of the algorithm.
关键词
机器学习/光学性能监测/光网络优化/神经网络/强化学习Key words
machine learning/optical performance monitoring/optical network optimization/neural network/reinforcement learning引用本文复制引用
基金项目
国家重点研发计划(2022YFB2903303)
出版年
2024
国家科技期刊平台
NSTL
万方数据