Learned Signal-to-Noise Ratio Estimation in Optical Fiber Communication Links

Mohamed Al-Nahhal, Ibrahim Al-Nahhal, Octavia A. Dobre, Sunish Kumar Orappanpara Soman, Deyuan Chang, Chuandong Li

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a signal-to-noise ratio (SNR)
estimator based on recurrent neural network (RNN) in optical
fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the This paper proposes a signal-to-noise ratio (SNR) estimator based on recurrent neural network (RNN) in optical
fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the received signal. The proposed input features do not require knowledge of the transmitted symbols. In the proposed SNR estimator, three different RNN models are investigated, namely simple RNN, gated recurrent units, and long short-term memory. The overall computational complexity of the three models of the proposed estimator, including the feature extraction and RNN structures, are analyzed. Numerical results show that the three models of the proposed estimator provide a trade-off between the complexity of the RNN structure and estimation accuracy. Furthermore, the proposed estimator achieves a better SNR estimation accuracy and reduces the overall computational complexity compared to the literature.
Original languageEnglish
Article number7260107
Pages (from-to)1-7
Number of pages7
JournalIEEE Photonics Journal
Volume14
Issue number6
Early online date15 Nov 2022
DOIs
Publication statusPublished (in print/issue) - 1 Dec 2022

Bibliographical note

Funding Information:
This work was supported by Huawei Technologies Canada Company, Ltd.

Publisher Copyright:
© 2009-2012 IEEE.

Keywords

  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics
  • Optical fiber communication links
  • performance monitoring
  • recurrent neural networks
  • complexity analysis

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