Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation

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.

© 2021 Optical Society of America

Intelligent constellation diagram analyzer using convolutional neural network-based deep learning

Danshi Wang, Min Zhang, Jin Li, Ze Li, Jianqiang Li, Chuang Song, and Xue Chen
Opt. Express 25(15) 17150-17166 (2017)

Joint, accurate and robust optical signal-to- noise ratio and modulation format monitoring scheme using a single Stokes-parameter-based artificial neural network

Qian Xiang, Yanfu Yang, Qun Zhang, and Yong Yao
Opt. Express 29(5) 7276-7287 (2021)

OSNR and nonlinear noise power estimation for optical fiber communication systems using LSTM based deep learning technique

Ziyi Wang, Aiying Yang, Peng Guo, and Pinjing He
Opt. Express 26(16) 21346-21357 (2018)

Convolutional Neural Network-Based Optical Performance Monitoring for Optical Transport Networks

Takahito Tanimura, Takeshi Hoshida, Tomoyuki Kato, Shigeki Watanabe, and Hiroyuki Morikawa
J. Opt. Commun. Netw. 11(1) A52-A59 (2019)

Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor

Danshi Wang, Mengyuan Wang, Min Zhang, Zhiguo Zhang, Hui Yang, Jin Li, Jianqiang Li, and Xue Chen
Opt. Express 27(7) 9403-9419 (2019)