AI-Empowered Software-Defined WLANs


The complexity of wireless and mobile networks is growing at an unprecedented pace. This trend is set to make current network control and management techniques based on analytical models and simulations impractical, especially if combined with the data deluge expected from future applications such as Augmented and Mixed Reality. This is particularly true for Software-Defined Wireless Local Area Networks (SD-WLANs). It is our belief that to cope with this increase in complexity, future SD-WLANs must follow an Artificial Intelligence-native approach. In this paper we present aiOS, which is an AI-based platform for the control and management of SD-WLANs. Our proposal is aligned with the most recent trends in in-network AI promoted by the Telecommunication Standardization Sector (ITU-T) and with the architecture for disaggregated radio access networks promoted by the Open Radio Access Network (ORAN) Alliance. We validate aiOS in a practical use case, namely frame size optimization in SD-WLANs, and we consider the longterm evolution, challenges, and scenarios for AI-assisted network automation in the wireless and mobile networking domain.

IEEE Communications Magazine