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 and Antenna Systems, Optical Networks

Marija Furdek Prekratic

Chalmers, Electrical Engineering, Communication and Antenna Systems, 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)

Vol. 13 4 B12-B20 409704

Safeguarding optical communication networks from cyber-security attacks

Swedish Research Council (VR), 2020-01-01 -- 2023-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

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

More information

Latest update

2/24/2021