Adaptive ML‑Based Frame Length Optimisation in Enterprise SD‑WLANs

Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems
with several practical implementations and numerous proposals being made. Despite
instigating a shift from monolithic network architectures towards more modulated
operations, automated network management requires the ability to extract, utilise
and improve knowledge over time. Beyond simply scrutinizing data, Machine
Learning (ML) is evolving from a simple tool applied in networking to an active
component in what is known as Knowledge-Defined Networking (KDN). This work
discusses the inclusion of ML techniques in the specific case of Software-Defined
Wireless Local Area Networks (SD-WLANs), paying particular attention to the
frame length optimization problem. With this in mind, we propose an adaptive MLbased
approach for frame size selection on a per-user basis by taking into account
both specific channel conditions and global performance indicators. By relying on
standard frame aggregation mechanisms, the model can be seamlessly embedded
into any Enterprise SD-WLAN by obtaining the data needed from the control plane,
and then returning the output back to this in order to efficiently adapt the frame size
to the needs of each user. Our approach has been gauged by analysing a multitude of
scenarios, with the results showing an average improvement of 18.36% in goodput
over standard aggregation mechanisms.