We demonstrate QoT estimation in a live network utilizing neural networks trained on synthetic data spanning a large parameter space. The ML-model predicts the measured lightpath performance with <0.5dB SNR error over a wide configuration range.

翻译：我们利用在大型参数空间合成数据方面受过培训的神经网络,在实时网络中展示了QoT估计值。ML模型预测测量光路性能时,在宽宽配置范围内有<0.5dB SNR错误。