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This paper presents a path-loss model based on a radio-frequency (RF) detection scheme for various drones using 5G aerial communication over an industrial, scientific, and medical radio band (ISM band) network. We considered three communication modes of the ISM band for the channel characteristics analysis: the DJI Enhanced Spread Spectrum Technology (DESST) protocol, Wi-Fi, and Bluetooth. The drone signal detection scheme extracts the drone signal from the environment mixed with the general signal. The drone DESST signal is identified through cross-correlation of the received signal. The Wi-Fi and Bluetooth signals are identified with the singular-value decomposition (SVD) algorithm by using the hopping characteristics. General and drone Wi-Fi signals are separated by in-phase/quadrature (I/Q) phase analysis over the measurement time. The windowed received signal strength indicator (RSSI) moving detection (WRMD) analysis identifies the drone Bluetooth signal according to the movement of the drone. The detected drone signal is channel modeled by the horizontal distance d according to the altitude θ. Finally, they verify their model by a ray-tracing simulation similar to the real environment. The model provides a simple and accurate prediction for designing future aerial communications systems according to changes in drone movement.

Empirical Path-Loss Modeling and a RF Detection Scheme for Various Drones