Measurement-Based Large-Scale Indoor Channel Modeling for Hovering UAV-to-Ground Wireless Links

Indoor autonomous drones rely on robust wireless connectivity to operate in GPS-denied environments; however, indoor radio-frequency (RF) propagation is strongly affected by multipath, blockage, and device instability. These effects become particularly critical for hovering unmanned aerial vehicles (UAVs), known as drones, where vibration and orientation variations can significantly alter the wireless channel. This paper presents a measurement-based characterization of large-scale indoor RF propagation, explicitly comparing stationary ground-to-ground links with hovering UAV-to-ground links under identical indoor conditions. Using a purpose-built ESP32-based measurement platform operating at 2.4 GHz, extensive RSSI and RSSI-referenced signal-to-noise ratio (SNR) proxy measurements are collected across a realistic office environment. Based on spatially averaged measurements, log-distance path-loss models are derived for both scenarios. Unlike prior indoor channel modelling studies that consider ground-to-ground communications, this work quantifies the effect of hovering-induced vibration and antenna orientation on large-scale indoor propagation, showing that hovering UAV-to-ground links exhibit a substantially higher path-loss exponent (2.46) than stationary ground-to-ground links (1.50), along with increased shadowing variability (approximately 6.9 dB versus 6.1 dB). In some indoor locations, UAV hovering introduces RSSI and SNR proxy degradations of up to 20 dB relative to the stationary baseline. These findings demonstrate that conventional indoor propagation models can significantly underestimate attenuation and variability in indoor UAV deployments, highlighting the need to explicitly account for hovering-induced effects when designing and planning indoor UAV communication systems.