Abstract
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.
Highlights
Drones refer to unmanned aerial vehicles (UAVs) that can be controlled by radio waves, which means that an aircraft can fly remotely or automatically without a person on board
When constructing a drone defense system, more than 90% of drone detection technology relies on RF detection and physical detection of radar, and complex sensing is performed after the RF detection alarm
For 5G aerial networks, detecting the movement of various drones is affected by the radio propagation characteristics
Summary
Drones refer to unmanned aerial vehicles (UAVs) that can be controlled by radio waves, which means that an aircraft can fly remotely or automatically without a person on board. When constructing a drone defense system, more than 90% of drone detection technology relies on RF detection and physical detection of radar, and complex sensing is performed after the RF detection alarm This RF detection technique has a disadvantage in that the accuracy is low when other signals exist in the same frequency band. The RF detection and path loss modeling of this paper focus on the short range wireless communication network. In the 2.4 GHz band, the signal identified according to the proposed RF detection scheme carries out path loss modeling according to three protocols. The authors point out the need for height-dependent parameters for describing the propagation channel of UAVs. In this paper, we propose a new modeling approach for the suburban communication channel, capable of capturing the mean path-loss between a drone channels characterization and endured shadowing statistics.
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