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

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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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Data Availability Statement

The data generated during and/or analyzed during the current study were generated in a simulator available at: https://github.com/rodrigo-tinini/5GPy. The dataset used in the research can be made available on reasonable request to the author.

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Authors and Affiliations

  1. Universidade Federal da Bahia, Ondina, Salvador, BA, Brazil

    Matias R. P. dos Santos, Tiago O. Januario & Gustavo B. Figueiredo

  2. Centro Universitário FEI, SBernardo do Campo, SP, Brazil

    Rodrigo I. Tinini, Tiago O. Januario & Gustavo B. Figueiredo

Authors
  1. Matias R. P. dos Santos
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  2. Rodrigo I. Tinini
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  3. Tiago O. Januario
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  4. Gustavo B. Figueiredo
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Correspondence to Matias R. P. dos Santos.

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The research has been partially developed at UFBA and was funded by the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB) and by CNPq 313057/2020-6. Research Funds for the Federal University of Bahia.

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dos Santos, M.R.P., Tinini, R.I., Januario, T.O. et al. Deep recurrent neural network for optical fronthaul dimensioning and proactive vBBU placement in CF-RAN. Photon Netw Commun 43, 59–73 (2022). https://doi.org/10.1007/s11107-022-00964-0

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