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Next generation of wireless local area networks (WLANs) will operate indense, chaotic and highly dynamic scenarios that in a significant number ofcases may result in a low user experience due to uncontrolled high interferencelevels. 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 thosecomplex scenarios. On top of SDN, the use of machine learning (ML) techniquesmay improve network resource usage and management by identifying feasibleconfigurations through learning. ML techniques can drive WLANs to reach optimalworking points by means of parameter adjustment, in order to cope withdifferent network requirements and policies, as well as with the dynamicconditions. In this paper we overview the work done in SDN for WLANs, as wellas the pioneering works considering ML for WLAN optimization. Finally, in orderto demonstrate the potential of ML techniques in combination with SDN toimprove the network operation, we evaluate different use cases forintelligent-based spatial reuse and dynamic channel bonding operation in WLANsusing Multi-Armed Bandits.

Combining Software Defined Networks and Machine Learning to enable Self Organizing WLANs