光通信技术2024，Vol.48Issue(3)：1-6,6.DOI:10.13921/j.cnki.issn1002-5561.2024.03.0001
光路传输质量智能预测技术
Intelligent prediction technology for optical path quality of transmission
摘要
Abstract
Addressing the challenge of traditional mathematical model-based quality of transmission(QoT)prediction methods struggling to simultaneously meet the demands of high precision and low computational complexity,this paper introduces three intelligent QoT prediction techniques for single optical paths,multiple optical paths,and cross-topology optical paths.These tech-niques rely on machine learning models to achieve accurate end-to-end optical path QoT predictions and effectively tackle the following challenges:firstly,how to select appropriate machine learning models and input features amidst the diversity of physi-cal layer parameters.Secondly,how to effectively capture the intricate relationships among optical paths.Thirdly,how to train and continuously optimize network models with limited samples.Finally,the article offers a glimpse into the future development directions of optical path QoT prediction technologies.关键词
光网络/光路传输质量/机器学习Key words
optical networks/optical path quality of transmission/machine learning分类
信息技术与安全科学引用本文复制引用
谷志群,周宇航,张佳玮,纪越峰..光路传输质量智能预测技术[J].光通信技术,2024,48(3):1-6,6.基金项目
国家自然科学基金项目(62101058、U21B2005)资助 （62101058、U21B2005）
河北省省级科技计划项目(22567624H)资助. （22567624H）
