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

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URI: https://hdl.handle.net/10016/40014
ISSN: 0733-8724
DOI: https://doi.org/10.1109/JLT.2022.3225827
UXXI: AR/0000033916
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2023-03-15
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IEEE
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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
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Deep neural network, Few-mode fiber, Quality of transmission estimation, Transmission estimation, Regression, Single-mode fiber
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Amirabadi, M. A., Kahaei, M. H., Nezamalhosseini, S. A., Arpanaei, F., & Carena, A. (2023). Deep Neural Network-Based QoT Estimation for SMF and FMF Links. Journal Of Lightwave Technology, 41(6), 1684-1695
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