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使用IQ星座图像和CNN在无网格WDM系统中估计光谱间距和线性ICI效应
Applied optics
|August 12, 2025
概括
在无网格WDM系统中准确估计光谱间距对于ROADM至关重要. 这项研究使用星座图热图和CNN来检测通道重叠和测量间距,从而实现高效的光网络管理.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 精确的光谱间距估计对于无网格波长分割多重复合 (WDM) 系统中的可重新配置光学加点多重复合器 (ROADM) 至关重要.
- 在运输节点的通道聚合过程中避免线性通道间干扰 (ICI) 需要精确的防护带管理.
- 动态无网格光学传输系统需要先进的监控工具来高效运行.
研究的目的:
- 提出一种用于识别无网格WDM系统中的光谱重叠光学通道的新方法.
- 准确估计相邻光学通道之间的光谱间距 (保护带).
- 开发一个能够在ROADM中做出快速决策的系统,以优化网络性能.
主要方法:
- 从星座图中构建热散射图像.
- 卷积神经网络 (CNN) 用于图像分析的应用.
- 在无网格16-QAM尼奎斯特-WDM系统中验证,频道间距变化和光信号噪声比 (OSNR).
主要成果:
- 覆盖检测达到3x16Gbaud的准确度.
- 频谱间距估计误差 (RMSE) 在0.6GHz以下.
- 最小的性能处罚 (~98%的准确度,~0.6 GHz RMSE) 即使没有OSNR知识.
结论:
- 拟议的热图和基于CNN的方法有效地识别了通道重叠,并估计了无网格WDM系统中的光谱间距.
- 这种技术为光学网络中的动态光谱管理提供了强大的解决方案.
- 该方法显示了将其集成到先进光学传输系统的未来监控工具中的巨大潜力.
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