Forecasting SNR margins has low-complexity
Forecasting SNR margins has low-complexity
Golaghazadeh, Firouzeh and Djukic, Petar and Tremblay, Christine and Desrosiers, Christian
Optics InfoBase Conference Papers 2020
Abstract : We use an extensive dataset from a production network to forecast signal-to-noise ratio (SNR) margin and show that a näive technique is able to forecast SNR margin better than a deep neural network (DNN).
