Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning
Nonlinear effects provide inherent limitations in fiber optical communications. Here, the authors experimentally demonstrate improved digital back propagation with machine learning and use the results to reveal insights in the optimization of digital signal processing.
Guardado en:
Autores principales: | Qirui Fan, Gai Zhou, Tao Gui, Chao Lu, Alan Pak Tao Lau |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/50f8d967bc4946a0b867e0de9bb7a6ad |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Correlated Eigenvalues of Multi-Soliton Optical Communications
por: Wen Qi Zhang, et al.
Publicado: (2019) -
Recent advances in nonlinear optics for bio-imaging applications
por: Zhang Silu, et al.
Publicado: (2020) -
Optical wave patterns of nonlinear Schrödinger equation with anti-cubic nonlinearity in optical fiber
por: Fan Sun
Publicado: (2021) -
An information-theoretic machine learning approach to expression QTL analysis.
por: Tao Huang, et al.
Publicado: (2013) -
Theoretical and Numerical Analysis of Coupled Axial-Torsional Nonlinear Vibration of Drill Strings
por: Xinye Li, et al.
Publicado: (2021)