Z. Wang, E. Akinrintoyo. Optical signal spectrum prediction using machine learning and in-line channel monitors in a multi-span ROADM system. Retrieved from https://par.nsf.gov/biblio/10457288. in Proc. ECOC’22, Sept. 2022 .

Z. Wang, E. Akinrintoyo. Optical signal spectrum prediction using machine learning and in-line channel monitors in a multi-span ROADM system. in Proc. ECOC’22, Sept. 2022, (). Retrieved from https://par.nsf.gov/biblio/10457288.

Z. Wang, E. Akinrintoyo. "Optical signal spectrum prediction using machine learning and in-line channel monitors in a multi-span ROADM system". in Proc. ECOC’22, Sept. 2022 (). Country unknown/Code not available. https://par.nsf.gov/biblio/10457288.

@article{osti_10457288,
place = {Country unknown/Code not available}, title = {Optical signal spectrum prediction using machine learning and in-line channel monitors in a multi-span ROADM system}, url = {https://par.nsf.gov/biblio/10457288}, abstractNote = {We measure the performance of separately characterized machine learning-based EDFA models for predicting the optical power spectrum evolution in a 5-span system with six ROADM nodes deployed in the COSMOS testbed, which achieve a mean absolute error of 0.6–0.7 dB after 10 EDFAs under varying channel loading configurations.}, journal = {in Proc. ECOC’22, Sept. 2022}, author = {Z. Wang, E. Akinrintoyo}, }