Feasibility of UAV Link Space Diversity in Wooded Areas

Šimůnek, Michal; Pechač, Pavel; Fontán, F.P.

doi:10.1155/2013/890629

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…For UAV communications, there are very few measurement campaigns on the effect of multiple antenna systems. In [43] and [44], the AG channel characterization was initiated with a 1 × 4 antenna configuration. In this work, carrier-to-noise ratio (CNR) gain was compared for the common combining strategies such as selection, equal-gain and maximal ratio combining (MRC).…”

Section: Diversity Gainmentioning

confidence: 99%

“…They have also presented a narrowband channel estimator capable of replicating signal dynamics. Some related measurement campaigns were conducted with similar equipment in Prague for path loss model in urban area [38] with a flight altitude between 150 to 300 m. Further, measurements in [61] and [62] were conducted in urban and wooded areas, respectively, to study space diversity techniques for similar situations.…”

Section: Measurement Campaignsmentioning

confidence: 99%

“…In the context of UAV communications, there are few measurement campaigns on the effect of multiple antenna elements. In [61] and [62], the AG channel characterization was initiated with a 1 × 4 antenna configuration. In this work, carrier-to-noise ratio (CNR) gain was compared for the common combining strategies such as selection, equal-gain and maximal ratio combining (MRC) with different antenna elements and observed that the diversity gain achieved with the MRC method using four antenna elements is approximately 4 dB greater in an urban environment than in a wooded area under similar circumstances.…”

Section: Diversity Gainmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

715

346

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Unmanned aerial vehicles (UAVs) have gained great interest for rapid deployment in both civil and military applications. UAV communication has its own distinctive channel characteristics compared with widely used cellular and satellite systems. Thus, accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication systems. However, several challenges exist in UAV channel modeling. For example, propagation characteristics of UAV channels are still less explored for spatial and temporal variations in non-stationary channels. Also, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey on the measurement campaigns launched for UAV channel modeling using low altitude platforms and discusses various channel characterization efforts. We also review the contemporary perspective of UAV channel modeling approaches and outline some future research challenges in this domain.

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Section: Diversity Gainmentioning

confidence: 99%

Section: Measurement Campaignsmentioning

confidence: 99%

Section: Diversity Gainmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

715

346

View full text Add to dashboard Cite

show abstract

“…3D channel modeling is based on the straight line distance, according to distance and altitude, and does not accurately reflect path loss due to altitude. The modeling work in [5][6][7][8][9][10][11][12][13] suggests that the altitude loss of a UAV is similar to the free space propagation model. The authors point out the need for height-dependent parameters for describing the propagation channel of UAVs.…”

Section: Introductionmentioning

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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“…Such a user can be characterized as being static for the time spent within a particular location. As satellites on nongeostationary Earth orbits, such as medium or low Earth orbits, provide the required high-data rates with small time delays, the position of a particular satellite, with respect to the user, may change dramatically within the period of time the user remains in a fixed position, in contrast to low elevation links such as in [1].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variations of Polarization Diversity Gain in a Vegetated Area considering High Elevation Angles and a Nomadic User

Kvičera

Pechač

2015

International Journal of Antennas and Propagation

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Seasonal variations of the polarization diversity gain are addressed for a nomadic user in a vegetated area taking high elevation angles and nongeostationary satellites into consideration. Corresponding experimental data were obtained at a frequency of 2.0 GHz at Stromovka Park in Prague, the Czech Republic, within the full in-leaf and out-of-leaf periods of 2013 and 2014, respectively. By detecting copolarized and cross-polarized components of the transmitted left-and right-handed circularly polarized signals, the corresponding diversity gain was obtained for multiple-input single-output (MISO), single-input multiple-output (SIMO), and combined MISO/SIMO cases. It was found that tree defoliation results in a significant decrease of the polarization diversity gain achieved for low time percentages in particular scenarios.

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Feasibility of UAV Link Space Diversity in Wooded Areas

Cited by 7 publications

References 9 publications

A Survey of Channel Modeling for UAV Communications

A Survey of Channel Modeling for UAV Communications

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

Seasonal Variations of Polarization Diversity Gain in a Vegetated Area considering High Elevation Angles and a Nomadic User

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