LiBRA

We conduct one of the first extensive experimental studies of the two main link adaptation mechanisms in 60 GHz WLANs, namely rate adaptation and beam adaptation. We first show, using a variety of commodity 60 GHz devices, that simple heuristics, used by these devices to determine which the two mechanisms should be triggered, can lead to wrong decisions even in seemingly simple scenarios. We then explore for first time the feasibility of leveraging PHY layer information and ML to guide link adaptation, using a large dataset collected with a 60 GHz software-define radio testbed in a variety of indoor environments and scenarios. Finally, we design LiBRA, a practical, standard-compliant link adaptation framework that leverages ML and PHY layer information to determine when to trigger link adaptation and which adaptation mechanism to use. LiBRA strikes a balance between throughput and link recovery delay, performing close to an oracle solution, and outperforming significantly two simple heuristics used by off-the-shelf devices.