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.

Keywords
EVM estimation, linear regression, coherent optical systems
DOI
10.1364/CLEOPR.2022.CMP9A_03
Hyperlink
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. In: 2022 Conference on Lasers and Electro-Optics Pacific Rim: Proceedings, Japan, Sapporo, 31 Aug-5 Sep., 2022. Washington: Optica Publishing Group, 2022, pp.1-7. Available from: doi:10.1364/CLEOPR.2022.CMP9A_03

Publication language
English (en)