Combining software defined networks and machine learning to enable self organizing wlans
Published in Unknown Venue, 2019
Recommended citation: Álvaro López-Raventós and Francesc Wilhelmi and Sergio Barrachina-Muñoz and Boris Bellalta (2019). Combining software defined networks and machine learning to enable self organizing wlans. Unknown Venue. https://ieeexplore.ieee.org/abstract/document/8923569/
Abstract: Next generation of wireless local area networks (WLANs) will operate in dense, chaotic and highly dynamic scenarios that in a significant number of cases may result in a low user experience due to uncontrolled high interference levels. Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide WLANs with new capabilities to deal with users’ demands, while achieving greater levels of efficiency and flexibility in those complex scenarios. On top of SDN, the use of machine learning (ML) techniques may improve network resource usage and management by identifying feasible configurations through learning. ML techniques can drive WLANs to reach optimal working points by means of parameter adjustment, in order to cope with different network requirements and policies, as well as with the dynamic conditions. In this paper we overview the work done in SDN for WLANs, as …