基于富里埃神经运算符的PDM连贯光传输系统的快速光纤非线性补偿方法
Optics express
|February 1, 2024
概括
一个新的神经网络模型快速补偿光学系统中的色谱分散和光纤非线性. 这种进步提高了信号质量,并大大减少了与传统方法相比的处理时间.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 光纤非线性补偿 (NLC) 对于扩大连贯光传输的覆盖范围和容量至关重要.
- 染色分散 (CD) 和纤维非线性降低了长距离信号质量.
研究的目的:
- 引入一种新的,快速的,黑子神经网络模型,用于同时进行CD和NLC.
- 评估模型在先进的连贯光通信系统中的性能.
主要方法:
- 使用基于福里埃神经运算符 (FNO) 的神经网络模型.
- 在32GBaud极化-分裂-多重复合 (PDM) 16/32/64-ary正方形振幅调制 (QAM) 信号上测试了该模型.
- 与传统的数字反传播 (DBP) 相比,性能比较.
主要成果:
- 与DBP相比,在1600公里SSMF的PDM-16QAM信号中实现了0.31dB的Q因子改进.
- 计算时间从6.04s (CPU) 减少到1.69s和1.54s (GPU) 减少到0.03s.
- 在不同的发射功率和调制格式中证明了通用性.
结论:
- 基于FNO的模型提供高效和有效的同时CD和NLC.
- 这种方法显著提高了处理速度,同时保持或提高了信号质量.
- 该模型在未来的高容量光学网络中显示出实际实施的希望.
相关概念视频
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