Experimental Evaluation of Air-to-Ground VHF Band Communication for UAV Relays

Unmanned Aerial Vehicles (UAVs) are a disruptive technology that is transforming a range of industries. Because they operate in the sky, UAVs are able to take advantage of strong Line-of-Sight (LoS) channels for radio propagation, allowing them to communicate over much larger distances than equivalent hardware located at ground level. This has attracted the attention of organisations such as the Irish Defence Forces (DF), with whom we are developing a UAV-based radio relay system as part of the MISTRAL project. This relay system will support digital Very High Frequency (VHF) band communication between ground personnel, while they are deployed on missions. In this paper we report on the initial set of experimental measurements which were carried out to verify the feasibility of VHF signal relaying via UAV. In our experiments, a UAV carrying a lightweight Software-Defined Radio (SDR) receiver is positioned at a height of 500 meters above ground, while two 5W transmitters travel in vehicles on the ground. The SDR receiver measures the received signal power, while the Global Positioning System (GPS) coordinates of the vehicles are logged. This is combined to measure the signal pathloss over distance. Our results show that the signal is received successfully at distances of over 50 kilometers away. While the signals still appear to suffer from a degree of obstacle blockage and multipath effects, these communication ranges are a substantial improvement over the ground communication baseline, and validate the use of UAVs to support wide area emergency communication.Comment: Pre-print of paper presented at the Workshop on Integrating UAVs into 5G and Beyond at IEEE International Conference on Communications 202

Millimeter Wave Channel Modeling via Generative Neural Networks

Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must capture the delay, directions, and path gains, for each link and with high resolution. This paper presents a general modeling methodology based on training generative neural networks from data. The proposed generative model consists of a two-stage structure that first predicts the state of each link (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder that generates the path losses, delays, and angles of arrival and departure for all its propagation paths. Importantly, minimal prior assumptions are made, enabling the model to capture complex relationships within the data. The methodology is demonstrated for 28GHz air-to-ground channels in an urban environment, with training datasets produced by means of ray tracing.Comment: Submitted to IEEE GLOBECOM 2020 Workshop on Wireless Propagation Channels for 5G and B5