Poster: Hawkeye - Predictive Positioning of a Ceiling-Mounted Mobile AP in mmWave WLANs for Maximizing Line-of-sight

Line-of-sight (LOS) is a critical requirement for mmWave communication. In this work, we make the case for a ceilingmounted mobile (CMM) AP by comparing its performance with other types of AP mobility and single static AP. We then present Hawkeye to solve the optimal location discovery problem for a CMM AP using a machine learning (ML) algorithm. Hawkeye relies purely on the connectivity matrix between STAs and the AP to decide if and where the AP should move to for maximizing LOS connectivity. Using a prototype implementation, we show that the throughput of Hawkeye is 219% and 129% compared with single static AP and other approaches for AP mobility, respectively.