TY - JOUR
T1 - AI-Empowered Software-Defined WLANs
AU - Coronado, Estefanía
AU - Bayhan, Suzan
AU - Thomas, Abin
AU - Riggio, Roberto
N1 - Funding Information:
Acknowledgments This work has been performed in the framework of the European Union’s Horizon 2020 project 5GZORRO co-funded by the EU under grant agreement No. 871533.
Publisher Copyright:
© 1979-2012 IEEE.
PY - 2021/3/3
Y1 - 2021/3/3
N2 - The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is proving current network control and management techniques based on analytical models and simulations to be impractical, especially if combined with the data deluge expected from future applications such as augmented reality. This is particularly true for software-defined wireless local area networks (SO-WLANs). It is our belief that to battle this growing complexity, future SO-WLANs must follow an artificial intelligence (AI) -native approach. In this article, we introduce aiOS, which is an AI-based platform that builds toward the autonomous management of SD-WLANs. Our proposal is aligned with the most recent trends in in-network AI promoted by the ITU Telecommunication Standardization Sector (ITU-T) and with the architecture for disaggregated radio access networks promoted by the Open Radio Access Network Alliance. We validate aiOS in a practical use case, namely frame size optimization in SD-WLANs, and we consider the long-term evolution, challenges, and scenarios for AI-assisted network automation in the wireless and mobile networking domain.
AB - The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is proving current network control and management techniques based on analytical models and simulations to be impractical, especially if combined with the data deluge expected from future applications such as augmented reality. This is particularly true for software-defined wireless local area networks (SO-WLANs). It is our belief that to battle this growing complexity, future SO-WLANs must follow an artificial intelligence (AI) -native approach. In this article, we introduce aiOS, which is an AI-based platform that builds toward the autonomous management of SD-WLANs. Our proposal is aligned with the most recent trends in in-network AI promoted by the ITU Telecommunication Standardization Sector (ITU-T) and with the architecture for disaggregated radio access networks promoted by the Open Radio Access Network Alliance. We validate aiOS in a practical use case, namely frame size optimization in SD-WLANs, and we consider the long-term evolution, challenges, and scenarios for AI-assisted network automation in the wireless and mobile networking domain.
KW - 2022 OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85105622767&partnerID=8YFLogxK
U2 - 10.1109/MCOM.001.2000895
DO - 10.1109/MCOM.001.2000895
M3 - Article
SN - 0163-6804
VL - 59
SP - 54
EP - 60
JO - IEEE communications magazine
JF - IEEE communications magazine
IS - 3
M1 - 9422336
ER -