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With the evolution of 5G technology, high definition video, virtual reality, and the internet of things (IoT), the demand for high capacity optical networks has been increasing dramatically. To support the capacity demand, low-margin optical networks engage operator interest. To engross this techno-economic interest, planning tools with higher accuracy and accurate models for the quality of transmission estimation (QoT-E) are needed. However, considering the state-of-the-art optical network’s heterogeneity, it is challenging to develop such an accurate planning tool and low-margin QoT-E models using the traditional analytical approach. Fortunately, data-driven machine-learning (ML) cognition provides a promising path. This paper reports the use of cross-trained ML-based learning methods to predict the QoT of an un-established lightpath (LP) in an agnostic network based on the retrieved data from already established LPs of an in-service network. This advanced prediction of the QoT of un-established LP in an agnostic network is a key enabler not only for the optimal planning of this network but it also provides the opportunity to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the generalized signal-to-noise ratio (GSNR), which includes the effect of both amplified spontaneous emission (ASE) noise and non-linear interference (NLI) accumulation. The real field data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated synthetic data set, supervised ML techniques such as wide deep neural network, deep neural network, multi-layer perceptron regressor, boasted tree regressor, decision tree regressor, and random forest regressor are applied, demonstrating the GSNR prediction of an un-established LP in an agnostic network with a maximum error of 0.40 dB.

Assessment of Cross-train Machine Learning Techniques for QoT-Estimation in agnostic Optical Networks / Khan, Ihtesham; Bilal, Muhammad; Curri, Vittorio. - In: OSA CONTINUUM. - ISSN 2578-7519. - 3:10(2020), pp. 2690-2706. [10.1364/OSAC.399511]

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