Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset

D. Uzunidis; C. Christofodis; I. De Francesca; J. M.Rivas Moscoso; D. Larrabeiti; J. M. Fabrega; D. M. Marom; I. Tomkos

doi:10.1364/OFC.2025.W2A.46

Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset

D. Uzunidis^*
, C. Christofodis
, I. De Francesca
, J. M.Rivas Moscoso
, D. Larrabeiti
, J. M. Fabrega
, D. M. Marom
, I. Tomkos

^*Corresponding author for this work

The Institute of Applied Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781557527370
DOIs	https://doi.org/10.1364/OFC.2025.W2A.46
State	Published - 2025
Event	2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - San Francisco, United States Duration: 30 Mar 2025 → 3 Apr 2025

Publication series

Name	2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings

Conference

Conference	2025 Optical Fiber Communications Conference and Exhibition, OFC 2025
Country/Territory	United States
City	San Francisco
Period	30/03/25 → 3/04/25

Bibliographical note

Publisher Copyright:
© 2025 Optica.

Access to Document

10.1364/OFC.2025.W2A.46

Cite this

Uzunidis, D., Christofodis, C., De Francesca, I., Moscoso, J. M. R., Larrabeiti, D., Fabrega, J. M., Marom, D. M., & Tomkos, I. (2025). Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset. In 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings (2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1364/OFC.2025.W2A.46

Uzunidis, D. ; Christofodis, C. ; De Francesca, I. et al. / Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset. 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings).

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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 = "אנגלית",

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",

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Uzunidis, D, Christofodis, C, De Francesca, I, Moscoso, JMR, Larrabeiti, D, Fabrega, JM, Marom, DM & Tomkos, I 2025, Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset. in 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings. 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025, San Francisco, United States, 30/03/25. https://doi.org/10.1364/OFC.2025.W2A.46

Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset. / Uzunidis, D.; Christofodis, C.; De Francesca, I. et al.
2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025. (2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Larrabeiti, D.

AU - Fabrega, J. M.

AU - Marom, D. M.

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T2 - 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025

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Uzunidis D, Christofodis C, De Francesca I, Moscoso JMR, Larrabeiti D, Fabrega JM et al. Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset. In 2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. (2025 Optical Fiber Communications Conference and Exhibition, OFC 2025 - Proceedings). doi: 10.1364/OFC.2025.W2A.46

Quantifying the Operational Benefits of Deep Learning Based Dynamic Traffic Prediction using Real-World Dataset

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