Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
Artikel i vetenskaplig tidskrift, 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.
Författare
Yuchuan Fan
RISE Research Institutes of Sweden
Kungliga Tekniska Högskolan (KTH)
Aleksejs Udalcovs
RISE Research Institutes of Sweden
Xiaodan Pang
RISE Research Institutes of Sweden
Kungliga Tekniska Högskolan (KTH)
Carlos Natalino Da Silva
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Marija Furdek Prekratic
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Sergei Popov
Kungliga Tekniska Högskolan (KTH)
Oskars Ozolins
RISE Research Institutes of Sweden
Kungliga Tekniska Högskolan (KTH)
Journal of Optical Communications and Networking
1943-0620 (ISSN) 19430639 (eISSN)
Vol. 13 4 B12-B20 409704Skydda optiska kommunikationsnätverk från cyber-säkerhetsattacker
Vetenskapsrådet (VR) (2019-05008), 2020-01-01 -- 2023-12-31.
Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier
Telekommunikation
DOI
10.1364/JOCN.409704
Relaterade dataset
2020_JOCN_CONSTELLATION_DATASET [dataset]
URI: https://ieee-dataport.org/documents/2020jocnconstellationdataset DOI: 10.21227/1684-a275