Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges
- DOI
- 10.1109/ojcoms.2022.3146618
- Published
- 2022
- Container
- IEEE Open Journal of the Communications Society
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1109/ojcoms.2022.3146618,
title = {Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges},
author = {Bouziane Brik and Karim Boutiba and Adlen Ksentini},
year = {2022},
journal = {IEEE Open Journal of the Communications Society},
doi = {10.1109/ojcoms.2022.3146618},
url = {https://doi.org/10.1109/ojcoms.2022.3146618}
}RIS
TY - JOUR TI - Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges AU - Bouziane Brik AU - Karim Boutiba AU - Adlen Ksentini PY - 2022 JO - IEEE Open Journal of the Communications Society DO - 10.1109/ojcoms.2022.3146618 UR - https://doi.org/10.1109/ojcoms.2022.3146618 ER -
APA
Brik, B., Boutiba, K., & Ksentini, A. (2022). Deep Learning for B5G Open Radio Access Network: Evolution, Survey, Case Studies, and Challenges. IEEE Open Journal of the Communications Society. https://doi.org/10.1109/ojcoms.2022.3146618
Source records
- crossref · retrieved 2026-09-25T04:15:53.940Z