Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems
IEEE Photonics Journal 2022
Yuchuan Fan, Xiaodan Pang, Aleksejs Udalcovs, Carlos Natalino, Lu Zhang, Sandis Spolītis, Vjačeslavs Bobrovs, Richard Schatz, Xianbin Yu, Marija Furdek, Sergei Popov, Oskars Ozoliņš

Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler.

Atslēgas vārdi
Deep learning, error vector magnitude, machine learning, optical fiber communication, optical performance monitoring

Fan, Y., Pang, X., Udalcovs, A., Natalino, C., Zhang, L., Spolītis, S., Bobrovs, V., Schatz, R., Yu, X., Furdek, M., Popov, S., Ozoliņš, O. Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems. IEEE Photonics Journal, 2022, Vol. 14, No. 4, Article number 8643108. e-ISSN 1943-0655. Available from: doi:10.1109/JPHOT.2022.3193727

Publikācijas valoda
English (en)
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