Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well the existence affordable highly interoperable devices. The Wi-Fi community is currently deploying 6 developing 7, which will bring higher data rates, better multi-user multi-AP support, and, most importantly, improved flexibility. These technical innovations, including plethora parameters, are making next-generation WLANs exceedingly complex dependencies between parameters joint optimization usually have non-linear impact on network performance. complexity further increased case dense deployments coexistence shared bands. While classical approaches fail such conditions, machine learning (ML) able handle complexity. Much research has been published using ML improve performance solutions slowly being adopted existing deployments. In this survey, we adopt structured approach describe various areas where applied. To end, analyze over 250 papers field, readers with an overview main trends. Based review, identify specific open challenges provide general future directions.