Aerial Channel Prediction and User Scheduling in Mobile Drone Hotspots

Abstract

In this paper, we investigate the aerial wireless channel, where a moving drone is deployed to stream content to a set of mobile clients on the ground over a small cell size. Experimental traces collected over more than twenty flights with multiple clients suggest that drone mobility in lateral or vertical path leads to time-selective and frequency-selective wireless channel for a low-altitude drone. The resulting aerial wireless channel can be predicted reasonably well when we model the channel based on the constructive and destructive interference patterns between the line-of-sight path and other propagation paths via nearby reflectors. We propose a novel channel prediction approach to predict the subcarrier SNRs for all clients as drone moves and a novel scheduling approach to select the subset of clients that maximize the network utility using the predicted SNRs. We have implemented the proposed approach on a commodity 802.11n chipset and evaluated in the field over twenty flights, each serving up to 17 live clients. Experiments demonstrate, for the first time, the feasibility of tracking and predicting the aerial Wi-Fi channel, resulting in up to a 56% increase in overall throughput as compared to the conventional 802.11n hotspot, while maintaining fairness across clients.