Feedforward Neural Network-Based EVM Estimation: Impairment Tolerance in Coherent Optical Systems
Journal article, 2022
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 conventional 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 next-generation intelligent OPM systems.
Optical noise
Estimation
Optical receivers
feedforward neural networks
Monitoring
optical fiber communication
monitoring
signal processing
Phase noise
Laser noise
Optical communication
Fiber optics
Author
Yuchuan Fan
RISE Research Institutes of Sweden
Royal Institute of Technology (KTH)
Xiaodan Pang
RISE Research Institutes of Sweden
Royal Institute of Technology (KTH)
Aleksejs Udalcovs
RISE Research Institutes of Sweden
Carlos Natalino Da Silva
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Lu Zhang
Zhejiang Lab
Zhejiang University
Vjaceslavs Bobrovs
Riga Technical University
Richard Schatz
Royal Institute of Technology (KTH)
Xianbin Yu
Zhejiang University
Zhejiang Lab
Marija Furdek Prekratic
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Sergei Popov
Royal Institute of Technology (KTH)
Oskars Ozolins
Royal Institute of Technology (KTH)
Riga Technical University
RISE Research Institutes of Sweden
IEEE Journal of Selected Topics in Quantum Electronics
1077-260X (ISSN) 15584542 (eISSN)
Vol. 28 4 6000410Providing Resilient & secure networks [Operating on Trusted Equipment] to CriTical infrastructures (PROTECT)
VINNOVA (2020-03506), 2021-02-01 -- 2024-01-31.
Subject Categories
Telecommunications
Control Engineering
Signal Processing
DOI
10.1109/JSTQE.2022.3177004