  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 well
as the pioneering works considering ML for WLAN optimization. Finally, in order
to demonstrate the potential of ML techniques in combination with SDN to
improve the network operation, we evaluate different use cases for
intelligent-based spatial reuse and dynamic channel bonding operation in WLANs
using Multi-Armed Bandits.
