Feedforward Neural Network-based EVM Estimation: Impairment Tolerance in Coherent Optical Systems
Journal of Selected Topics in Quantum Electronics 2022
Yuchuan Fan, Xiaodan Pang, Aleksejs Udalcovs, Carlos Natalino, Lu Zhang, Vjačeslavs Bobrovs, Richard Schatz, Xianbin Yu, Marija Furdek, Sergei Popov, Oskars Ozoliņš

Error vector magnitude (EVM) is commonly used for evaluating the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques for EVM estimation extend the functionality of onventional optical performance monitoring (OPM). In this article, we evaluate the tolerance of our developed EVM estimation scheme against various impairments in coherent optical systems. In particular, we analyze the signal quality monitoring capabilities in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise. We use feedforward neural networks (FFNNs) to extract the EVM information from amplitude histograms of 100 symbols per IQ cluster signal sequence captured before carrier phase recovery. We perform simulations of the considered impairments, along with an experimental investigation of the impact of laser phase noise. To investigate the tolerance of the EVM estimation scheme to each impairment type, we compare the accuracy for three training methods: 1) training without impairment, 2) training one model for all impairments, and 3) training an independent model for each impairment. Results indicate a good generalization of the proposed EVM estimation scheme, thus providing a valuable reference for developing nextgeneration intelligent OPM systems.

Atslēgas vārdi
communication, optical fiber communication, feedforward neural networks, signal processing, monitoring

Fan, Y., Pang, X., Udalcovs, A., Natalino, C., Zhang, L., Bobrovs, V., Schatz, R., Yu, X., Furdek, M., Popov, S., Ozoliņš, O. Feedforward Neural Network-based EVM Estimation: Impairment Tolerance in Coherent Optical Systems. Journal of Selected Topics in Quantum Electronics, 2022, Vol. 28, No. 4, Article number 6000410. ISSN 1077-260X. e-ISSN 1558-4542. Available from: doi:10.1109/JSTQE.2022.3177004

Publikācijas valoda
English (en)
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