基于深度神经网络的EON自适应调制方法研究  

Adaptive Modulation Method of EON based on Deep Neural Network

作　　者：王建华[1] 冉煜琨 赵杰[1,2] WANG Jian-hua;RAN Yu-kun;ZHAO Jie(School of Engineering and Technology,Chengdu University of Technology,Leshan 614000,China;Southwestern Institute of Physics,Chengdu 610213,China)

机构地区：[1]成都理工大学工程技术学院,四川乐山614000 [2]核工业西南物理研究院,成都610213

出　　处：《光通信研究》2021年第5期36-40,44,共6页Study on Optical Communications

基　　金：四川省重点实验室开放基金资助项目(scsxdz2019zd01)。

摘　　要：为了在弹性光网络(EON)上最大化总频谱效率,提高用户体验质量(QoE),文章提出了一种基于深度神经网络(DNN)的自适应调制方法。首先对光纤中的网络传输质量进行估计,然后利用深度学习方法,将路由长度、跳数和视频质量作为请求特征的3个参数,根据每个需求的期望QoE,选择具有最大频谱效率的调制进制。仿真结果表明,所提方法的平均频谱效率在美国国家自然科学基金会(NSF)网络中比距离自适应和整数规划方法分别高了51%和32%,在中国网络(CN)骨干网中分别高了43%和29%;且所提方法的阻塞概率比距离自适应和整数规划方法最低降低0.01。In order to maximize the total spectrum efficiency and improve the Quality of Experience(QoE)on Elastic Optical Network(EON),an adaptive modulation method based on Deep Neural Network(DNN)is proposed.Firstly,the network transmission quality in optical fiber is estimated.Then,the route length,hop number and video quality are taken as the three parameters of the request characteristics,and the deep learning method is adopted to selected the modulation system with the maximum spectrum efficiency according to the desired QoE of each requirement.The simulation results show that the average spectral efficiency of the proposed method is 51%and 32%higher in National Science Foundation(NSF)network than that of distance adaptive method and integer programming method,and 43%and 29%higher in China Network(CN)backbone network.In addition,the blocking probability of the proposed method is 0.01 lower than that of the distance adaptive method and integer programming method.

关 键 词：弹性光网络 体验质量 深度神经网络 频谱效率 自适应调制 

分 类 号：TN915[电子电信—通信与信息系统]