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

TL;DR

This paper explores how combining software-defined networking and machine learning can enhance the management and performance of dense, dynamic WLANs, demonstrating potential improvements through specific use cases.

Contribution

It provides an overview of SDN and ML applications in WLANs and evaluates ML-based techniques for spatial reuse and channel bonding using Multi-Armed Bandits.

Findings

ML techniques can optimize WLAN configurations dynamically.

SDN enables flexible and efficient WLAN management.

Use cases show potential improvements in network performance.

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 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.