基于异步延迟采样的光通信性能监测方法

Optical Fiber Communication Performance Monitoring Based on Asynchronous Delay Tap Sampling

基于异步延迟采样和人工神经网络统计学习提出了一种光通信性能监测方法。通过对高速光信号进行异步延迟采样,获得信号二维幅度直方图,然后提取其中特征参数并对人工神经网络进行训练,最后以人工神经网络的预测输出实现对光信号损伤的监测。构建10Gb/s非归零码开关键控,40Gb/s光学双二进制码和归零码差分移相键控光通信仿真系统,并对光信噪比、色散和偏振模色散损伤进行监测。仿真结果表明,所提方法对被监测光信号的速率、码型调制格式透明,可同时准确监测多种并存的传输损伤,损伤参数监测误差小于5%。该方法具有电域处理带宽要求低、采样机制简单的特点,适用于分布式在线光性能监测。

Based on asynchronous delay tap sampling and artificial neural network statistical machine learning, a novel optical performance monitoring （OPM） technique is proposed. The signal is delay tap sampled to obtain twodimensional histogram. Then the features of histograms are extracted to train the artificial neural networks. The outputs of trained neural network are used to monitor optical signal impairments. Simulations of optical signal-tonoise ratio, chromatic dispersion and polarization mode dispersion monitoring in 10 Gb/s nonreturn to zero code-onoff keying, 40 Gb/s optical doubinary code and return to zero-differential phase shift keying systems are presented. The simulation results show that the proposed scheme can monitor multiple simultaneous impairments on optical signals of diverse bit rates and formats with high accuracy, from which the monitoring error is less than 5 %. The proposed technique is simple, cost-effective and suitable for in-service distributed OPM.