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.

In this paper, we conduct an extensive experimental study of the two primary link adaptation mechanisms in 60 GHz WLANs, namely rate adaptation and beam adaptation, using a large data set collected from a 60 GHz software-defined radio testbed. First, we compare the effectiveness of the two mechanisms in a variety of indoor environments and scenarios, including linear and angular displacement, mobility, blockage, and interference. Next, we study the effectiveness of two rate adaptation approaches -- SNR-based rate adaptation, which has been proposed by recent works, and a learning-based approach using PHY layer information. Our results show that the former performs poorly in practical scenarios, while the latter is promising, especially when combined with online training. Finally, we explore the effectiveness of maintaining backup beams to speedup link recovery and reduce the beam training overhead. We show that this heuristic fails in scenarios involving angular displacement on the receiver side but is quite effective in most other scenarios.