带有参数输入的光纤传输模型基于基于生成的预训练物理信息的神经网络的参数输入
Optics express
|January 29, 2025
概括
一个新的原则驱动的光纤传输模型为短距离光通信提供了高效,通用的解决方案. 它实现了高精度,不需要为不同的比特率或预先收集的数据进行重新训练.
科学领域:
- 光学通信是指光学通信.
- 计算物理 计算物理
- 信号处理 信号处理
背景情况:
- 精确的光纤传输建模对于高速数据通信至关重要.
- 现有的车型往往需要广泛的再培训,以适应不同的传动参数.
- 开发具有计算效率和物理接地模型仍然是一个挑战.
研究的目的:
- 为短距离通信引入基于原则的光纤传输模型.
- 为了使各种比特速率的通用解决方案,而无需重新训练模型.
- 提高光学建模中的计算效率和物理解释性.
主要方法:
- 作为核心解决方案,利用了之前提出的基于原则的光纤模型.
- 应用了减少基础扩展方法,以实现高效的计算.
- 将参数化输入转换为非线性施罗丁格方程的系数.
主要成果:
- 通过对模型系数进行参数化,实现了各种比特速率的通用解决方案.
- 在计算效率和物理背景方面展示了显著的优势.
- 在不需要预先收集的传输信号的情况下成功训练了模型.
- 通过使用100公里以上的开关和脉冲振幅调制信号验证了模型忠实度.
结论:
- 开发的模型为短距离光纤传输提供了一种高效和多功能方法.
- 它提供了显著的计算节约,并保持了强大的物理接地.
- 该模型在没有先前信号数据的情况下进行训练的能力提高了其实际适用性.
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