2026-04-14T04:49:24Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/5315392025-01-03T10:43:47Zcom_2072_355069com_2072_4427col_2072_355136

00925njm 22002777a 4500 dc Bayhan, Suzan author Coronado, Estefanía author Riggio, Roberto author Thomas, Abin author 2021-05-03 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 like Augmented and Mixed Reality. This is especially true for Software-Defined WLANs (SD-WLANs). It is our standpoint that to tame this increase in complexity, future SD-WLANs must follow an Artificial Intelligence (AI) native approach. In this paper, we present aiOS, an AI-based platform for SD-WLANs control and management. Our proposal is aligned with the most recent trends in in-network AI pushed by ITU-T and with the architecture for disaggregated radio access networks pushed by O-RAN. We validate aiOS in a practical use case, namely frame size optimisation in SD-WLANs, and elaborate on the long term evolution, challenges, and scenarios for AI-assisted network automation in the wireless and mobile networking domain. http://hdl.handle.net/2072/531539 10.1109/MCOM.001.2000895 AI-Empowered Software-Defined WLANs