A Comparison of Linear Regression and Deep Learning Model for EVM Estimation in Coherent Optical Systems
2022 Conference on Lasers and Electro-Optics Pacific Rim: Proceedings
2022
Yuchuan Fan,
Xiaodan Pang,
Aleksejs Udaļcovs,
Carlos Natalino,
Lu Zhang,
Sandis Spolītis,
Vjačeslavs Bobrovs,
Richard Schatz,
Xianbin Yu,
Marija Furdek,
Sergei Popov,
Oskars Ozoliņš
We experimentally investigate EVM estimation approaches based on linear regression and deep learning for 28 Gbaud coherent optical systems. We show that the estimation performances are comparable when the modulation format is known.
Atslēgas vārdi
EVM estimation, linear regression, coherent optical systems
DOI
10.1364/CLEOPR.2022.CMP9A_03
Hipersaite
https://opg.optica.org/abstract.cfm?URI=CLEOPR-2022-CMP9A_03
Fan, Y., Pang, X., Udaļcovs, A., Natalino, C., Zhang, L., Spolītis, S., Bobrovs, V., Schatz, R., Yu, X., Furdek, M., Popov, S., Ozoliņš, O. A Comparison of Linear Regression and Deep Learning Model for EVM Estimation in Coherent Optical Systems. No: 2022 Conference on Lasers and Electro-Optics Pacific Rim: Proceedings, Japāna, Sapporo, 31. aug.-5. sept.., 2022. Washington: Optica Publishing Group, 2022, 1.-7.lpp. Pieejams: doi:10.1364/CLEOPR.2022.CMP9A_03
Publikācijas valoda
English (en)