An Analytical Model for Assessing the Performance of IEEE 802.11ad Beamforming Training

In the race for Gigabit wireless links, IEEE introduced 802.11ad, an amendment aimed at delivering Gbps capacities in a WLAN setting by leveraging the 60 GHz band. The key innovation of the standard is its beamforming training protocol. Executed periodically at the beginning of every beacon interval, it enables the formation of directional links. To address contention during the uplink part of beamforming training, 802.11ad introduced A-BFT (Access-BeamForming Training), an Aloha-inspired, two-level backoff race. While central to the functionality of 802.11ad networks, the performance of A-BFT, however, remains poorly understood. In this paper, we propose an analytical finite-population model for evaluating the performance of 802.11ad A-BFT under the assumption of ideal channel conditions. After using an open-source simulator to demonstrate its accuracy, we use our model to assess the performance of A-BFT. We find that a counterintuitive, quit-easily/be-lazy approach by the stations leads to the best overall beamforming training performance.