Characterization of the Human Body Impact on UAV-to-Ground Channels at Ultra-Low Altitudes

Abstract

At ultra-low altitudes, an unmanned aerial vehicle (UAV) can act as a personal base station where it communicates only with one user. User equipment (UE) can be inside the pocket of a user or near their chest while facing or facing-away from the flying base station. In these scenarios, the wireless channel can go through different fading levels, depending on the UAV's location, user orientation, the location of the UE near the user's body, and the frequency of the transmitted signal. In this work, we provide measurement results and investigate how the human body affects the Air-to-Ground (AtG) channel characteristics under various use cases of holding a device. These channel characteristics include the average signal strength, shadowing, multipath, and the Rician K-factor. We target three different use cases of holding the device: Near-Chest Facing, In-pocket Facing, and Near-Chest Facing-away from the transmitting UAV. We perform this study at carrier frequencies of 900 MHz and 2.5 GHz and in Line-of-Sight conditions. First, we conduct a set of baseline experiments to understand ground-to-ground and AtG channels in free space. Second, we conduct AtG experiments with the user holding the device and show that the human body can induce either gains or losses compared to free space, depending on its relative orientation to the UAV. Third, we find that there are two distinct regions of operation, one in which the channel characteristics are mainly affected by the UAV and another that is dominated by the user's body. Fourth, we address the time-varying nature of the K-factor and analyze the user's body impact on its mean and standard deviation. We find that if a user changes their orientation to face away from the transmitting UAV, their body can cause extreme fluctuations in the K-factor value over time and reduce its mean value by an average and a maximum of 6.8 and 15 dB, respectively. Finally, to demonstrate the impact of our findings on the design of deployment strategies of UAVs, we considered how the human body and the relative UAV hovering position can affect the physical layer security in a UAV-assisted network. We show that the secrecy rate for a UAV-based network can be heavily influenced by the human body orientation relative to the UAV hovering location, consequently resulting in a different optimal deployment strategy compared to existing schemes that employ free-space pathloss models that neglect such human-induced effects.