基于混合式机器学习的光信噪比预测方法

OSNR Prediction Method for based on Hybrid Machine Learning

随着网络技术的蓬勃发展,光网络向着超高速大容量的方向发展,光网络结构越来越复杂,导致网络中光信号传输受到各种物理层损伤的影响。在光通信设备中,物理层最重要的参数就是光信噪比(OSNR),其值的大小直接决定了业务能否正常运行,一旦不能满足要求,将会造成传输错误或失败、服务质量降低和传送消耗增加等问题。文章提出一种准确高效的光通信节点OSNR预测方法,将基于解析式的先验知识方法和基于深度学习的后验知识方法结合起来,提出了一种基于混合式机器学习算法的光通信节点OSNR预测方法,用先验知识去降低神经网络的训练代价,提供高准确度的OSNR预测,研究表明,文章所提方法可以在更苛刻的条件下提供高准确度的机器学习模型。

With the vigorous development of network technology,optical networks are developing in the direction of ultra-high-speed and large-capacity.Since the structure of the optical network is becoming more complex.the optical signal will inevitably suffer various impairments in the optical network.In optical communication equipment,the most important parameter of the physical layer is the Optical Signal-to-Noise Ratio(OSNR).Its value directly determines whether the service can operate normally.Once the requirements are not met,it will cause transmission errors or failures,reducing service quality,and transmission consumption.This paper proposes an accurate and efficient OSNR prediction method for optical communication nodes.By combining analytic prior knowledge methods and deep learning-based posterior knowledge methods,an optical communication node OSNR based on hybrid machine learning algorithms are proposed.The prediction method uses prior knowledge to reduce the training cost of neural networks and provides high-accuracy OSNR predictions.The result shows that the method proposed in this paper can provide high-accuracy machine learning models under more demanding conditions.