Physics-oriented learning of nonlinear Schr\"odinger equation: optical fiber loss and dispersion profile identification
Physics-oriented learning of nonlinear Schr\"odinger equation: optical fiber loss and dispersion profile identification
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发表时间：
2021-04
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通讯作者：
T. Sasai;M. Nakamura;E. Yamazaki;Shuto Yamamoto;H. Nishizawa;Y. Kisaka
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作者：
T. Sasai;M. Nakamura;E. Yamazaki;Shuto Yamamoto;H. Nishizawa;Y. Kisaka
在光纤通信中，长期以来，非线性薛定格方程（NLSE）的系统识别（SI）主要用于光纤非线性补偿（NLC）。最近的一项研究是将数字反向传播（DBP）等行为模型方法与神经网络（NN）等数据驱动方法相结合。这些工作的目的是获得更多的 NLC 增益；然而，通过将我们的注意力集中在这种 SI 过程中学习到的参数上，系统状态信息（即光纤参数）可能会在这里，我们展示了基于神经网络的 DBP 中学习参数的基于模型的优化和可解释的性质可以实现传输线路监控，从而完全提取实际的在线 NLSE 参数分布。具体来说，我们证明可以直接从数据承载信号中获得沿多跨度链路的纵向损耗和色散剖面，而无需任何专用的模拟设备（例如光时域反射计），并且我们将该方法应用于长距离（约 2,080 公里）链路和各种链路条件。测试，包括插入的额外损耗、不同的光纤输入功率和非均匀电平图，还从测量范围、精度和光纤发射功率方面研究了测量性能，这些结果为简化和自动化网络管理提供了一条途径，作为 DBP 的另一个应用。
In optical fiber communication, system identification (SI) for the nonlinear Schr\"odinger equation (NLSE) has long been studied mainly for fiber nonlinearity compensation (NLC). One recent line of inquiry to combine a behavioral-model approach like digital backpropagation (DBP) and a data-driven approach like neural network (NN). These works are aimed for more NLC gain; however, by directing our attention to the learned parameters in such a SI process, system status information, i.e., optical fiber parameters, will possibly be extracted. Here, we show that the model-based optimization and interpretable nature of the learned parameters in NN-based DBP enable transmission line monitoring, fully extracting the actual in-line NLSE parameter distributions. Specifically, we demonstrate that longitudinal loss and dispersion profiles along a multi-span link can be obtained at once, directly from data-carrying signals without any dedicated analog devices such as optical time-domain reflectometry. We apply the method to a long-haul (~2,080 km) link and various link conditions are tested, including excess loss inserted, different fiber input power, and non-uniform level diagram. The measurement performance is also investigated in terms of measurement range, accuracy, and fiber launch power. These results provide a path toward simplified and automated network management as another application of DBP.