Analysis of the radiowave propagation in an underground mine based on the modified Ray launching method

Kubal, Sławomir; Staniec, Kamil; Zieliński, Ryszard

doi:10.1049/iet-map.2014.0807

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Figures 30-32 present the ray-tracing simulation screen at different distances and altitudes, respectively. The Wi-Fi and DESST models were compared with the 802.11b standard model, and the Bluetooth model was compared with the 802.15.1 standard model [25][26][27][28]. Figures 33 and 34 show the raytracing simulation results of each standard model and the proposed model.…”

Section: Verification Of the Proposed Modelmentioning

confidence: 99%

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

Won

Choi

Kim

2018

Wireless Communications and Mobile Computing

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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.

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Section: Verification Of the Proposed Modelmentioning

confidence: 99%