A QoT Estimation Method using EGN-assisted Machine Learning for Network Planning Applications
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An ML model based on precomputed per-channel SCI is proposed. Due to its superior accuracy over closed-form GN, an average SNR gain of 1.1 dB in an end-to-end link optimization and a 40 traffic requests in a network planning scenario are shown.
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