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Published in: Photonic Network Communications 1/2022

09-02-2022 | Original Paper

Deep recurrent neural network for optical fronthaul dimensioning and proactive vBBU placement in CF-RAN

Authors: Matias R. P. dos Santos, Rodrigo I. Tinini, Tiago O. Januario, Gustavo B. Figueiredo

Published in: Photonic Network Communications | Issue 1/2022

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Abstract

In this paper, we solve virtualized passive optical network (VPON) assignment and virtualized baseband unit (vBBU) placement using an integer linear programming formulation, an approximated heuristic using linear relaxation, and a proactive heuristic based on a specific kind of recurrent neural network. We also studied the application of multi-step forecasting in Cloud-Fog Radio Access Network (CF-RAN) traffic demands for joint use with integer linear programming once it allows the solver more time to generate solutions. Also, we examine if the error in batch prediction impacts the final solution in terms of blocking and correctness.
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Metadata
Title
Deep recurrent neural network for optical fronthaul dimensioning and proactive vBBU placement in CF-RAN
Authors
Matias R. P. dos Santos
Rodrigo I. Tinini
Tiago O. Januario
Gustavo B. Figueiredo
Publication date
09-02-2022
Publisher
Springer US
Published in
Photonic Network Communications / Issue 1/2022
Print ISSN: 1387-974X
Electronic ISSN: 1572-8188
DOI
https://doi.org/10.1007/s11107-022-00964-0