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}

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}

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}






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}




%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%% AI/ML %%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%
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}

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}

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 author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V},
 booktitle = {Advances in Neural Information Processing Systems},
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 pages = {3104--3112},
 title = {{Sequence to Sequence Learning with Neural Networks}},
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}


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      archivePrefix={arXiv},
      primaryClass={cs.LG}
}




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}




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@ARTICLE{9422344,
  author={Li, Xi and Garcia-Saavedra, Andres and Costa-Perez, Xavier and Bernardos, Carlos J. and Guimarães, Carlos and Antevski, Kiril and Mangues-Bafalluy, Josep and Baranda, Jorge and Zeydan, Engin and Corujo, Daniel and Iovanna, Paola and Landi, Giada and Alonso, Jesus and Paixão, Paulo and Martins, Hugo and Lorenzo, Manuel and Ordonez-Lucena, Jose and López, Diego R.},
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  pages={84-90},
  doi={10.1109/MCOM.001.2000730}}


@inproceedings{daemon,
author = {Banchs, Albert and Fiore, Marco and Garcia-Saavedra, Andres and Gramaglia, Marco},
title = {{Network Intelligence in 6G: Challenges and Opportunities}},
year = {2021},
isbn = {9781450387064},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477091.3482761},
doi = {10.1145/3477091.3482761},
abstract = {The success of the upcoming 6G systems will largely depend on the quality of the Network
Intelligence (NI) that will fully automate network management. Artificial Intelligence
(AI) models are commonly regarded as the cornerstone for NI design, as they have proven
extremely successful at solving hard problems that require inferring complex relationships
from entangled, massive (network traffic) data. However, the common approach of plugging
‘vanilla’ AI models into controllers and orchestrators does not fulfil the potential
of the technology. Instead, AI models should be tailored to the specific network level
and respond to the specific needs of network functions, eventually coordinated by
an end-to-end NI-native architecture for 6G. In this paper, we discuss these challenges
and provide results for a candidate NI-driven functionality that is properly integrated
into the proposed architecture: network capacity forecasting.},
booktitle = {Proceedings of the 16th ACM Workshop on Mobility in the Evolving Internet Architecture},
pages = {7–12},
numpages = {6},
keywords = {network intelligence, 6G, mobile networks},
location = {New Orleans, Louisiana},
series = {MobiArch '21}
}


@INPROCEEDINGS{hexa-x,  author={Uusitalo, Mikko A. and Ericson, Mårten and Richerzhagen, Björn and Soykan, Elif Ustundag and Rugeland, Patrik and Fettweis, Gerhard and Sabella, Dario and Wikström, Gustav and Boldi, Mauro and Hamon, Marie-Helene and Schotten, Hans D. and Ziegler, Volker and Strinati, Emilio Calvanese and Latva-aho, Matti and Serrano, Pablo and Zou, Yaning and Carrozzo, Gino and Martrat, Josep and Stea, Giovanni and Demestichas, Panagiotis and Pärssinen, Aarno and Svensson, Tommy},  booktitle={2021 Joint European Conference on Networks and Communications   6G Summit (EuCNC/6G Summit)},   title={Hexa-X The European 6G flagship project},   year={2021},  volume={},  number={},  pages={580-585},  doi={10.1109/EuCNC/6GSummit51104.2021.9482430}}


@misc{5gppp-annual,
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    note = {Online; accessed 28 October 2021} ,
    year=2021,
}


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  publisher={Springer}
}


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@article{natureteleoperation,
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  volume={111},
  number={3},
  pages={1077--1097},
  year={2020},
  publisher={Springer}
}


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  volume={10},
  number={5},
  pages={691--703},
  year={2005},
  publisher={Springer}
}

@article{bianchi,
  title={{Performance analysis of the IEEE 802.11 distributed coordination function}},
  author={Bianchi, Giuseppe},
  journal={IEEE Journal on selected areas in communications},
  volume={18},
  number={3},
  pages={535--547},
  year={2000},
  publisher={IEEE}
}


@article{ciw,
  author    = {Geraint I. Palmer and Vincent A. Knight and Paul R. Harper and Asyl L. Hawa},
  title     = {Ciw: An open-source discrete event simulation library},
  journal   = {Journal of Simulation},
  volume    = {13},
  number    = {1},
  pages     = {68-82},
  year      = {2019},
  publisher = {Taylor & Francis},
  doi       = {10.1080/17477778.2018.1473909},
}





%% REFs after IEEE TNSM 1st review

@book{time-series-adfuller,
  title={Time series analysis},
  author={Hamilton, James Douglas},
  year={2020},
  publisher={Princeton university press}
}

@book{econometric-analysis-granger,
  title={Econometric analysis},
  author={Greene, William H},
  year={2003},
  publisher={Pearson Education India}
}



@book{introduction-time-series,
  title={New introduction to multiple time series analysis},
  author={L{\"u}tkepohl, Helmut},
  year={2005},
  publisher={Springer Science \& Business Media}
}

@book{time-series-theory,
  title={Time series: theory and methods},
  author={Brockwell, Peter J and Davis, Richard A},
  year={2009},
  publisher={Springer Science \& Business Media}
}

@book{box-jenkins,
  title={Time series analysis: forecasting and control},
  author={Box, George EP and Jenkins, Gwilym M and Reinsel, Gregory C and Ljung, Greta M},
  year={2015},
  publisher={John Wiley \& Sons}
}

@article{aicc,
  title={Further analysts of the data by akaike's information criterion and the finite corrections: Further analysts of the data by akaike's},
  author={Sugiura, Nariaki},
  journal={Communications in Statistics-theory and Methods},
  volume={7},
  number={1},
  pages={13--26},
  year={1978},
  publisher={Taylor \& Francis}
}







@techreport{5g-acias,
    title={{5G for Connected Industries and Automation}},
    author={{5G ACIAS}},
    year={2019},
    month={{February}},
    type={White Paper}
}

@ARTICLE{foreco,
    author={Groshev, Milan and Martín-Pérez, Jorge and Guimarães, Carlos and de la Oliva, Antonio and Bernardos, Carlos J.},
    journal={IEEE Transactions on Network and Service Management},   title={{FoReCo: a forecast-based recovery mechanism for real-time remote control of robotic manipulators}},   year={2022},  volume={},  number={},  pages={1-1},  doi={10.1109/TNSM.2022.3173436}}

@ARTICLE{deep,  author={Guimarães, Carlos and Groshev, Milan and Cominardi, Luca and Zabala, Aitor and Contreras, Luis M. and Talat, Samer T. and Zhang, Chao and Hazra, Saptarshi and Mourad, Alain and de la Oliva, Antonio},  journal={IEEE Communications Magazine},   title={DEEP: A Vertical-Oriented Intelligent and Automated Platform for the Edge and Fog},   year={2021},  volume={59},  number={6},  pages={66-72},  doi={10.1109/MCOM.001.2000986}}


@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@inproceedings{ros,
  title={ROS: an open-source Robot Operating System},
  author={Quigley, Morgan and Conley, Ken and Gerkey, Brian and Faust, Josh and Foote, Tully and Leibs, Jeremy and Wheeler, Rob and Ng, Andrew Y and others},
  booktitle={ICRA workshop on open source software},
  volume={3},
  number={3.2},
  pages={5},
  year={2009},
  organization={Kobe, Japan}
}