AI-Empowered Software-Defined WLANs

Estefanía Coronado, Suzan Bayhan, Abin Thomas, Roberto Riggio

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
167 Downloads (Pure)


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.
Original languageEnglish
Article number9422336
Pages (from-to)54-60
Number of pages7
JournalIEEE communications magazine
Issue number3
Publication statusPublished - 3 Mar 2021


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