Deep Neural Network-Based QoT Estimation for SMF and FMF Links Articles uri icon

authors

  • AMIRABADI, MOHAMMAD ALI
  • KAHAEI, MOHAMMAD HOSSEIN
  • NEZAMALHOSSEINI, S. ALIREZA
  • ARPANAEI, FARHAD
  • CARENA, ANDREA

publication date

  • March 2023

start page

  • 1684

end page

  • 1695

issue

  • 6

volume

  • 41

International Standard Serial Number (ISSN)

  • 0733-8724

Electronic International Standard Serial Number (EISSN)

  • 1558-2213

abstract

  • Quality of transmission (QoT) estimation tools for
    fiber links are the enabler for the deployment of reconfigurable
    optical networks. To dynamically set up lightpaths based on traffic
    request, a centralized controller must base decisions on reliable
    performance predictions. QoT estimation methods can be categorised in three classes: exact analytical models which provide
    accurate results with heavy computations, approximate formulas
    that require less computations but deliver a reduced accuracy, and
    machine learning (ML)-based methods which potentially have high
    accuracy with low complexity. To operate an optical network in
    real-time, beside accurate QoT estimation, the speed in delivering
    results is a strict requirement. Based on this, only the last two
    categories are candidates for this application. In this paper, we
    present a deep neural network (DNN) structure for QoT estimation considering both regular single-mode fiber (SMF) and future
    few-mode fiber (FMF) proposed to increase the overall network
    capacity. We comprehensively explore ML-based regression methods for estimating generalized signal-to-noise ratio (GSNR) in
    partial-load SMF and FMF links. Synthetic datasets have been
    generated using the enhanced Gaussian noise (EGN) model. Results
    indicate that the proposed DNN-based regressor can provide better
    accuracy along with less computation complexity, compared with
    other state-of-the-art ML methods as well as closed-form-EGN and
    closed-form-GN models

keywords

  • deep neural network; few-mode fiber; quality of; transmission estimation; regression; single-mode fiber