Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
Journal article, 2021
We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks (CNN) 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 (AWGN) impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase shift keying (QPSK), 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 (BER) estimation, enabling solutions for intelligent optical performance monitoring.
Author
Yuchuan Fan
RISE Research Institutes of Sweden
Royal Institute of Technology (KTH)
Aleksejs Udalcovs
RISE Research Institutes of Sweden
Xiaodan Pang
RISE Research Institutes of Sweden
Royal Institute of Technology (KTH)
Carlos Natalino Da Silva
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Marija Furdek Prekratic
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Sergei Popov
Royal Institute of Technology (KTH)
Oskars Ozolins
RISE Research Institutes of Sweden
Royal Institute of Technology (KTH)
Journal of Optical Communications and Networking
1943-0620 (ISSN) 19430639 (eISSN)
Vol. 13 4 B12-B20 409704Safeguarding optical communication networks from cyber-security attacks
Swedish Research Council (VR) (2019-05008), 2020-01-01 -- 2023-12-31.
Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2011)
Telecommunications
DOI
10.1364/JOCN.409704
Related datasets
2020_JOCN_CONSTELLATION_DATASET [dataset]
URI: https://ieee-dataport.org/documents/2020jocnconstellationdataset DOI: 10.21227/1684-a275