@inproceedings{6b852b0dce304fecbbe7a6efc9575067,
title = "Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset",
abstract = "A convolutional neural network is trained using real-world data, for dynamic prediction of the required transceivers supporting 6 G X -haul, leading to 20\% and 16\% lower average transceiver utilization over static and semi-static cases, respectively.",
author = "D. Uzunidis and C. Christofodis and \{De Francesca\}, I. and Moscoso, \{J. M.Rivas\} and D. Larrabeiti and Fabrega, \{J. M.\} and Marom, \{D. M.\} and I. Tomkos",
note = "Publisher Copyright: {\textcopyright} 2025 Optica.; 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 ; Conference date: 30-03-2025 Through 03-04-2025",
year = "2025",
doi = "10.1364/OFC.2025.W2A.46",
language = "English",
series = "2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings",
address = "United States",
}