2021/04/13 by Takeo Sasai, Masanori Nakamura, Sasai, Takeo +9 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Biology #Dispersion (optics) #FOS: Electrical engineering #FOS: Physical sciences #Fiber #Identification (biology) #Materials science #Neural Networks and Reservoir Computing #Nonlinear Schrödinger equation #Nonlinear system #Optical Network Technologies #Optical fiber #Optics #Optics (physics.optics) #Photonic and Optical Devices #Physics #Quantum mechanics #Signal Processing (eess.SP) #Statistical physics #eess.SP #electronic engineering #information engineering #physics.optics
paper · pdf · doi:10.48550/arxiv.2104.05890
published in arXiv (Cornell University) (Cornell University) · 26 pages, 10 figures
arxiv created 2021/04/13 · openalex publication_date 2021/04/13 · arxiv updated 2021/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In optical fiber communication, system identification (SI) for the nonlinear Schrödinger 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.