Guangtongxin yanjiu (Jun 2024)
Optical Network Monitoring and Optimization Methods based on Machine Learning
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.
