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Deep neural network based OSNR and availability predictions for multicast light-trees in optical WDM networks
Optics Express ( IF 3.8 ) Pub Date : 2020-03-27 , DOI: 10.1364/oe.388337
Xin Li , Lu Zhang , Jianghua Wei , Shanguo Huang

The quality of transmission (QoT) of a light-tree is influenced by a variety of physical impairments including attenuation, dispersion, amplified spontaneous emission (ASE), nonlinear effect, light-splitting, etc. Moreover, a light-tree has multiple destinations that have different distances away from the source node so that the QoT of the received optical signal at each destination is different from each other. Since the optical network is a living network, the real-time network state is difficult to obtain. Therefore, it is difficult to accurately and rapidly determine the QoT or availability of a light-tree. However, the QoT or availability of a light-tree obtained in advance not only guarantees the quality of service (QoS) but also helps to network optimization design. This paper studies the problems of the optical signal-to-noise ratio (OSNR) and availability predictions for multicast light-trees while leveraging deep neural network (DNN) in optical WDM networks. The DNN based OSNR and availability prediction methods are developed and implemented. Numerical results show that the DNN based OSNR prediction method reaches an accuracy of about 95%. And the DNN based availability prediction method reaches a high accuracy greater than 98%. These two methods provide a fast decision approach for light-tree construction.

中文翻译:

WDM网络中基于深度神经网络的OSNR和多播光树的可用性预测

轻树的传输质量(QoT)受各种物理损伤的影响,包括衰减,色散,放大的自发发射(ASE),非线性效应,分光等。此外,轻树有多个目标它们与源节点的距离不同,因此在每个目的地接收的光信号的QoT互不相同。由于光网络是生活网络,因此很难获得实时网络状态。因此,难以准确,快速地确定轻树的QoT或可用性。但是,预先获得的QoT或轻树的可用性不仅可以保证服务质量(QoS),而且有助于网络优化设计。本文研究了在光WDM网络中利用深度神经网络(DNN)的同时,对多播光树的光信噪比(OSNR)和可用性预测的问题。开发并实现了基于DNN的OSNR和可用性预测方法。数值结果表明,基于DNN的OSNR预测方法可达到约95%的精度。并且基于DNN的可用性预测方法可达到98%以上的高精度。这两种方法为轻树的构建提供了一种快速的决策方法。数值结果表明,基于DNN的OSNR预测方法可达到约95%的精度。并且基于DNN的可用性预测方法可达到98%以上的高精度。这两种方法为轻树的构建提供了一种快速的决策方法。数值结果表明,基于DNN的OSNR预测方法可达到约95%的精度。并且基于DNN的可用性预测方法可达到98%以上的高精度。这两种方法为轻树的构建提供了一种快速的决策方法。
更新日期:2020-03-31
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