基于机器学习的光纤链路网络风险分析与路由预测

A. Garg, Raghvendra Singh Deval, Karvy Mohnot, Anushka Joshi, V. Janyani, M. Aly
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引用次数: 0

摘要

在部署之前预测光纤链路的风险是优化网络设计的一个重要步骤。本文试图将丰富数据的力量扩展到光纤网络领域,根据道路密度、障碍物密度、水密度等物理因素,预测候选路径建立后可能存在的风险,从而识别光纤实施风险,获得更好的路径。
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ML Based Risk Analysis and Route Prediction for Optical Fibre Link Network
Predicting the risk of an optical fibre link prior to its deployment is a step of capital importance for an optimised design of the network. In this paper, we have tried to expand the power of abundant data into the field of optical fibre networks by predicting the probable risk post establishment of a candidate path based on its physical factors such as road density, obstruction density and water density, etc. for identifying the fibre implementation risk and achieving a better route.
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