IEEE Open Journal of the Communications Society (Jan 2021)

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

  • Andrea D'Amico,
  • Stefano Straullu,
  • Giacomo Borraccini,
  • Elliot London,
  • Stefano Bottacchi,
  • Stefano Piciaccia,
  • Alberto Tanzi,
  • Antonino Nespola,
  • Gabriele Galimberti,
  • Scott Swail,
  • Vittorio Curri

DOI
https://doi.org/10.1109/OJCOMS.2021.3066913
Journal volume & issue
Vol. 2
pp. 564 – 574

Abstract

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Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a software-defined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signal-to-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented.

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