摘要
提出了一种基于信号光谱和多任务深度神经网络(MT-DNN)的多信道并行光性能监测(OPM)方案,采集多信道光谱图进行预处理来设计幅度直方图(Ahs),可实现波分复用(WDM)系统多信道调制格式识别(MFI)和光信噪比(OSNR)监测。在建立的3信道WDM相干光通信系统中,对由PDM-4QAM/16QAM/64QAM组合的10种调制格式的3信道信号实现了MFI准确率为100%、OSNR监测的平均绝对误差(MAE)为0.16 dB的精准监测。为进一步研究所提OPM方案的性能以应对复杂的传输环境,提出了迁移学习辅助的多任务深度神经网络(TL-MT-DNN)用于多信道MFI和OSNR并行监测。结果表明,所提方案可移植性较好,还可节省大量样本和训练周期,其MFI准确率仍可达100%,3信道OSNR监测的MAE分别为0.24 dB、0.20 dB和0.19 dB。
Objective As emerging services have a higher demand for internet performance,high-capacity,multi-channel,and flexible fiber optic communication systems have become the trend of optical communications with the advantages of dynamic,high-capacity,and transparent transmission.Complex link impairments in large-capacity and multi-channel optical communication systems put forward higher requirements for optical performance monitoring(OPM)technology.The number of monitoring parameters and links of OPM needs to be increased continuously with a higher monitoring accuracy and a larger dynamic range.In the previous papers,existing monitoring mechanisms for optical fiber communications focus on OPM performance and are still dominated by single-channel monitoring schemes.The so-called multi-channel monitoring schemes are operated sequentially by selecting specific channels through tunable optical filters,which may introduce measurement delays for multi-channel systems such as wavelength division multiplexing(WDM)systems.Besides,in next-generation dynamically reconfigurable optical networks,OPM is also conducted on intermediate nodes except for the receiver.Obviously,there are few studies on this flexible OPM.In order to meet these demands for future OPM schemes,it is necessary to develop OPM that can be used for multi-channel monitoring with portability,low complexity,and high accuracy.Therefore,a simplified multi-channel parallel OPM scheme is proposed based on deep learning to overcome the shortcomings in multi-channel monitoring.Methods In this paper,a multi-channel parallel OPM scheme based on signal spectrum and multi-task deep neural network(MT-DNN)is proposed to deal with the shortcomings of the multi-channel OPM.This scheme processes the collected multi-channel spectrum from the fiber link by downsampling,filtering,signal waveform separation,and power normalization.Then,the number of signal sample points is counted based on each power value interval to generate amplitude histograms(Ahs).The Keras library in the Tensor
作者
李梦岩
吴锦涛
杨静宇
张力夫
谭勇
邱天
李岳彬
邓鹤鸣
罗风光
杨柳
Li Mengyan;Wu Jintao;Yang Jingyu;Zhang Lifu;Tan Yong;Qiu Tian;Li Yuebin;Deng Heming;Luo Fengguang;Yang Liu(School of Microelectronics,Hubei University,Wuhan 430062,Hubei,China;National Engineering Research Center of Nect Generation Internet Access-System,School of Optical and Electronic Information,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2023年第7期131-141,共11页
Acta Optica Sinica
基金
国家自然科学基金(62001181)
国家重点研发计划(2022YFB2903000)
湖北省重点研发计划(2022BAA007)
武汉市知识创新专项-曙光计划(202201080102335)。
关键词
机器视觉
光性能监测
波分复用
光信噪比
调制格式识别
迁移学习
多任务深度神经网络
machine vision
optical performance monitoring
wavelength division multiplexing
optical signal-to-noise ratio
modulation format identification
transfer learning
multi-task deep neural network
