Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
Yuchuan Fan,Aleksejs Udalcovs,Xiaodan Pang,Carlos Natalino,Marija Furdek,Sergei Popov,Oskars Ozolins +6 more
TLDR
A regression model is built to extract EVM information from complex signal constellation diagrams using a small number of received symbols to be used as a low-complexity alternative to conventional bit-error-rate estimation, enabling solutions for intelligent optical performance monitoring.Abstract:
We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks for error vector magnitude (EVM) estimation in coherent optical communications. We build a regression model to extract EVM information from complex signal constellation diagrams using a small number of received symbols. For the additive-white-Gaussian-noise-impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase-shift keying, 2.2% for 16-ary quadrature amplitude modulation (16QAM), and 1.1% for 64QAM signals, requiring only 100 symbols per constellation cluster in each observation period. Therefore, it can be used as a low-complexity alternative to conventional bit-error-rate estimation, enabling solutions for intelligent optical performance monitoring.read more
Citations
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Journal ArticleDOI
Monitoring and diagnostic technologies usingdeep neural networks for predictive optical network maintenance [Invited]
TL;DR: In this paper, the authors describe a series of workflows for the whitebox transponder, including getting optical performance data from the coherent optical transceiver, diagnosing optical transmission line conditions by applying deep neural networks (DNNs) to the collected data, and notifying the remote network management system (NMS) of the diagnosis results.
Journal ArticleDOI
Feedforward Neural Network-Based EVM Estimation: Impairment Tolerance in Coherent Optical Systems
Yuchuan Fan,Xiaodan Pang,Aleksejs Udalcovs,Carlos Natalino,Lu Zhang,Vjaceslavs Bobrovs,Richard Schatz,Xianbin Yu,Marija Furdek,Sergei Popov,Oskars Ozolins +10 more
TL;DR: In this article , the authors evaluate the error vector magnitude (EVM) estimation of coherent optical systems in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise.
Journal ArticleDOI
Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems
Yuchuan Fan,Xiaodan Pang,Aleksejs Udalcovs,Carlos Natalino,Lu Zhang,Sandis Spolitis,Vjaceslavs Bobrovs,Richard Schatz,Xianbin Yu,Marija Furdek,Sergei Popov,Oskars Ozolins +11 more
TL;DR: This work performs both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme and paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler.
Journal ArticleDOI
DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
TL;DR: In this paper , a CSI-free blind DNN detector was proposed for GOMIMO systems, where a specially designed deep neural network (DNN)-based MIMO detector mainly consists of two modules: one is the pre-processing module which is designed to address both the path loss and channel crosstalk issues caused by MIMI transmission, and the feed-forward DNN module is used for joint detection of spatial and constellation information by learning the statistics of both the input signal and the additive noise.
Journal ArticleDOI
Experimental validation of CNNs versus FFNNs for time- and energy-efficient EVM estimation in coherent optical systems
Yuchuan Fan,Aleksejs Udalcovs,Carlos Natalino,Xiaodan Pang,Richard Schatz,Marija Furdek,Sergei Popov,Oskars Ozolins +7 more
TL;DR: In this article, two deep learning-based estimation schemes were proposed for error vector magnitude (EVM) estimation for mQAM signal quality monitoring, which can be used to perform time-sensitive and accurate EVM estimation.
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