Nonlinear interference mitigation via deep neural networks
Publication
, Journal Article
Häger, C; Pfister, HD
Published in: Optics InfoBase Conference Papers
January 1, 2018
A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32×100 km fiber-optic link, the resulting “learned“ DBP significantly reduces the complexity compared to conventional DBP implementations.
Duke Scholars
Published In
Optics InfoBase Conference Papers
DOI
EISSN
2162-2701
Publication Date
January 1, 2018
Volume
Part F84-OFC 2018
Citation
APA
Chicago
ICMJE
MLA
NLM
Häger, C., & Pfister, H. D. (2018). Nonlinear interference mitigation via deep neural networks. Optics InfoBase Conference Papers, Part F84-OFC 2018. https://doi.org/10.1364/OFC.2018.W3A.4
Häger, C., and H. D. Pfister. “Nonlinear interference mitigation via deep neural networks.” Optics InfoBase Conference Papers Part F84-OFC 2018 (January 1, 2018). https://doi.org/10.1364/OFC.2018.W3A.4.
Häger C, Pfister HD. Nonlinear interference mitigation via deep neural networks. Optics InfoBase Conference Papers. 2018 Jan 1;Part F84-OFC 2018.
Häger, C., and H. D. Pfister. “Nonlinear interference mitigation via deep neural networks.” Optics InfoBase Conference Papers, vol. Part F84-OFC 2018, Jan. 2018. Scopus, doi:10.1364/OFC.2018.W3A.4.
Häger C, Pfister HD. Nonlinear interference mitigation via deep neural networks. Optics InfoBase Conference Papers. 2018 Jan 1;Part F84-OFC 2018.
Published In
Optics InfoBase Conference Papers
DOI
EISSN
2162-2701
Publication Date
January 1, 2018
Volume
Part F84-OFC 2018