智能光性能监测实验和仿真数据

该成果通过实验和商用仿真软件VPI仿真方式搭建不同调制格式的相干通信系统，对每种调制格式和不同OSNR的数据进行强度和差分相位分析并使用Python构建的深度学习网络实现光性能监测。通过调制格式识别精度和OSNR的监测误差等验证模型的准确性。实际建模实现的光性能监测结果数据由Python软件得到。最后，由Matlab对数据进行做图处理。支撑论文“Intelligent Optical Performance Monitoring Based on Intensity and Differential-Phase Features for Digital Coherent Receivers”以及“Joint Modulation Format Identification and OSNR Monitoring Based on Stokes Vector Distribution Features”This work constructed coherent communication systems with different modulation formats via experiments and commercial simulation software VPI. For data corresponding to each modulation format and varying OSNR values, intensity and differential phase analysis were conducted, and optical performance monitoring (OPM) was implemented using a deep learning network built with Python. The model's accuracy was validated using metrics such as modulation format recognition accuracy and OSNR monitoring error. The optical performance monitoring results derived from the practical modeling were obtained via Python. Finally, data visualization was performed using Matlab. This work supports the two academic papers: "Intelligent Optical Performance Monitoring Based on Intensity and Differential-Phase Features for Digital Coherent Receivers" and "Joint Modulation Format Identification and OSNR Monitoring Based on Stokes Vector Distribution Features".

搜集汇总

背景与挑战

背景概述

该数据集基于实验和仿真方式构建相干通信系统，采用深度学习网络实现光性能监测，并通过调制格式识别精度和OSNR监测误差验证模型准确性。数据包含14.33GB的103个文件，格式包括zip、txt、pdf等，支撑相关研究论文。