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A comparison between black-, grey- and white-box modeling for the bidirectional Raman amplifier optimization

arXiv 2310.05954 ↗First posted 11 Sep 2023physics.app-ph cs.CE cs.LGcited by 0

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Abstract (arXiv)

Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput. Offline optimization of optical amplifiers relies on models ranging from white-box models deeply rooted in physics to black-box data-driven and physics-agnostic models. Here, we compare the capabilities of white-, grey- and black-box models on the challenging test case of optimizing a bidirectional distributed Raman amplifier to achieve a target frequency-distance signal power profile. We show that any of the studied methods can achieve similar frequency and distance flatness of between 1 and 3.6 dB (depending on the definition of flatness) over the C-band in an 80-km span. Then, we discuss the models' applicability, advantages, and drawbacks based on the target application scenario, in particular in terms of flexibility, optimization speed, and access to training data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 38 references that are in the map. First posted on arXiv 11 Sep 2023.

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References · 4

  1. 2021Spectral and spatial power evolution design with machine learning-enabled Raman amplificationcited by 4
  2. 2022Physics-informed EDFA Gain Model Based on Active Learningcited by 1
  3. 2022Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Modelcited by 1
  4. 2022Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifierscited by 1

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