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Unmanned Aerial Vehicle Propagation Channel over Vegetation and Lake Areas: First- and Second-Order Statistical Analysis
Deyvid L. Leite, Pablo Javier Alsina, Millena M. de M. Campos, Vicente A. de Sousa Junior, Alvaro A. M. de Medeiros
TL;DR
Reliable UAV air-ground communication requires channel characterization because fading and Doppler can degrade signals in vegetation and lake environments. The paper measures a 915 MHz link using a Phantom 3 Standard and XBee across Caatinga and lake scenarios, analyzing large- and small-scale statistics. It finds distinct altitude-dependent path-loss behavior, Weibull small-scale fading, and usable Doppler-based speed estimation, while identifying low-altitude water effects as requiring further study.
Problem
UAV communication channels need characterization because environmental fading and mobility-induced Doppler can degrade signal reliability, particularly in Caatinga vegetation and lake settings.
Method
The study measures a 915 MHz Phantom 3 Standard–XBee link across lake, Caatinga, and combined scenarios at different heights and speeds, analyzing path loss, shadowing, fading distributions, and LCR.
Results
The channel shows negative path-loss exponents at lower heights and positive exponents at higher heights, while the KS test identifies Weibull as the best small-scale fading distribution at 95% significance.
Takeaways & Limitations
LCR can estimate Doppler frequency and drone speed, with lake flights at 3 km/h and 8 m providing better discrimination between speed and Doppler estimates.
Takeaways & Limitations
Low-altitude flights near water showed high path-loss exponents, and the influence of the water surface requires further analysis because related work is limited.
Abstract
from arXiv · showhide
The use of unmanned aerial vehicles (UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency.
1. Introduction
UAV air-ground links require channel characterization because vegetation, terrain, water, fading, and Doppler can impair reliable communication. The paper studies these effects in Caatinga and lake environments using first- and second-order statistics.
- Communication challenges: UAV communication links can suffer signal degradation from vegetation-induced multipath and mobility-induced Doppler, increasing inter-symbolic interference and BER.Vegetation creates multiple propagation paths, while Doppler spreads the signal and causes rapid amplitude and phase changes.
- Propagation environments: Reliable communication is especially challenging in environments involving relief variation, vegetation, urbanization, and reflecting or scattering lake surfaces.The Caatinga biome is a large Brazilian tropical region with limited prior radio-channel characterization, while lake surfaces can alter electromagnetic propagation.
- Research gap: The paper addresses limited Doppler-focused UAV channel studies by jointly identifying large- and small-scale channel effects in Caatinga and lake scenarios.Prior work largely emphasized path loss and shadowing, whereas this study also examines Doppler scattering through level crossing rate.
- Study scope: Measurements use a Phantom 3 Standard with an XBee transmitter at 915 MHz across lake, Caatinga, and combined environments at different heights and speeds.The analysis characterizes path loss, shadowing, fading distributions, and Doppler-related level crossing rates.
2. Materials
The measurement system combines a DJI Phantom 3 Standard UAV carrying an XBee transmitter with a ground base station that records received signal strength. Autonomous flights follow programmed routes at selected heights and speeds while packets are collected for channel analysis.
- 2.1. Unmanned Aerial Vehicle: The campaign uses autonomous DJI Phantom 3 Standard flights because waypoints and route speed can be predefined.The aircraft has approximately 25 minutes of battery autonomy and a maximum manual-control range of 1000 m.
- 2.2. XBee Module: The UAV carries an XBee 900HP PRO S3 module providing configurable-power wireless communication through an omnidirectional 2 dBi antenna.The module supports transmission powers from 10 to 250 mW, rates from 10 to 200 kbps, and receiver sensitivity down to −110 dBm.
- 2.2. XBee Module: Measurements use unicast IEEE 802.15.4 communication at 250 mW, with packets sent every 300 ms during constant-height, constant-speed flight segments.The UAV flies to programmed heights of 8 or 80 m while the ground station measures received packets.
- 2.2. XBee Module: The base station combines an XBee receiver, microcontroller, and computer to capture received signal strength and save measurement data.The microcontroller forwards signal-strength measurements from received packets to the computer.
- Measurement setup: Figure 1 depicts the reception-and-storage chain, while Figure 2 shows the UAV equipment and the base station used in the campaigns.The UAV-side equipment transmits data; the base station receives, processes, and stores it.
3. Mathematical Formulation and Methodology
The methodology separates large- and small-scale fading, fits candidate statistical models, and uses level-crossing behavior to estimate Doppler frequency and relative aircraft speed.
- 3. Mathematical Formulation and Methodology: Measurements with received-packet success below 98% were discarded because their low SNR produced poor agreement between empirical and theoretical distributions.
- 3.1. Filtering: A moving-average filter separates small-scale fading from large-scale attenuation using a symmetric window with N = M.The window is selected empirically as the smallest one whose output passes a 95% KS test for at least one candidate fading distribution.
- 3.2. Large-Scale Attenuation: Large-scale attenuation is modeled with path loss plus zero-mean log-normal shadowing, with the path-loss exponent estimated by linear regression.The model uses distance d, reference distance d0, path-loss exponent n, and shadowing standard deviation σ.
- 3.3. Small-Scale Fading: Maximum likelihood estimation fits Rayleigh, Rice, Nakagami, and Weibull distributions, which are compared with empirical CDFs using a KS test at confidence of at least 95%.This comparison also determines the filtering-window size.
- 3.3.1. Level Crossing Rate: The selected fading distribution supplies a theoretical level-crossing rate used to estimate Doppler spread.The candidate LCR functions correspond to Rayleigh, Rice, Nakagami, and Weibull fading models.
- 3.3.1. Level Crossing Rate: The Rician K parameter denotes dominant line-of-sight power relative to multipath power, while Nakagami m counts multipath clusters and Weibull β represents channel non-linearity.
- 3.3.2. Doppler Frequency: Comparing theoretical and empirical LCR values enables Doppler-frequency estimation, with normalized theoretical LCR obtained by setting fd = 1.
- 3.3.2. Doppler Frequency: Relative speed is estimated as v̂ = λ · f̂d / cos(θ), and the estimated Doppler frequency can differ for each evaluated power level.These equations calculate second-order statistics at a given power level.
4. Measurement Scenario
Measurements were conducted in Macaíba, Brazil, across lake, Caatinga, and mixed regions under varying wind and flight conditions. The scenarios used straight-line flights beginning 5 m horizontally from the base station, with heights and speeds defined per scenario.
- 4. Measurement Scenario: Campaigns in Macaíba recorded wind and temperature, while strong gusts altered low-speed UAV trajectories, instantaneous speed, and positioning.Weather data came from a local station.
- 4.1. Measurement Scenario 1: Lake: The lake scenario covered approximately 12,000 m^2 with maximum depth 12 m and used straight-line flights from a point 5 m horizontally from the BS.Measurement heights and speeds were specified in Table 3.
- 4.1. Measurement Scenario 1: Lake: Lake flights were photographed at 8 m and 80 m, with the flight direction and BS-to-lake area shown in Figure 3.The wind blew laterally across the aircraft and opposite the flight direction.
- 4.2. Measurement Scenario 2: Caatinga: The Caatinga scenario covered approximately 37,000 m^2 and included two 80 m flights at 1 and 3 km/h.Strong wind acted along and laterally to the flight path, requiring different travel distances for safety.
- 4.2. Measurement Scenario 2: Caatinga: The Caatinga flight region and aerial view were documented in Figure 4, including the area traversed by the UAV.
- 4.3. Mixed Scenario: The mixed region was tangent to the lake and included a small Caatinga area, with 80 m flights at 1 and 3 km/h starting 5 m horizontally from the BS.Figure 5 shows the tangent region and an image captured over the vegetation; the scenario parameters appear in Table 5.
5. Results
The measurements characterize large- and small-scale fading across lake, Caatinga, and mixed environments, including path loss, shadowing, distribution fitting, Doppler, and speed estimation. Results show altitude- and wind-dependent behavior, with Weibull providing the best small-scale fading fit.
- Large-scale fading: At lower altitudes, path loss has a negative exponent and shadowing shows higher standard deviation, indicating more severe large-scale fading across the measured environments.The study reports this behavior even over the lake surface.
- Small-scale fading: Weibull was the best-fit distribution for the measured small-scale fading at a 95% significance level, with theoretical and empirical CDFs showing good agreement.Parameters for Nakagami, Rice, Rayleigh, and Weibull distributions were estimated by maximum likelihood before the KS comparison.
- Small-scale fading: Lower speed and lower height produced greater small-scale fading variation, whereas flights at 80 m showed little variation across speeds in the lake, Caatinga, and mixed settings.The lower-speed, lower-height variation was associated with reduced flight stabilization during gusts.
- Doppler and speed estimation: LCR-based processing estimated Doppler frequency and aircraft speed from small-scale fading, using theoretical LCR values and the 915 MHz wavelength.The estimated speed used v̂ = λ · f̂d / cos(θ), with λ approximately 0.32 m.
- Doppler and speed estimation: The Doppler estimate was similar across most lake flights, except at 8 m and 3 km/h, where a more constant and higher speed produced a higher Doppler frequency.In Caatinga and mixed environments, wind prevented significant Doppler differences between nominal speeds.
6. Conclusions
The study characterizes air-ground channels in three environments by measuring large- and small-scale fading and using LCRs to estimate Doppler frequency and drone speed. It finds stronger low-altitude fading, Weibull small-scale behavior, and Doppler estimates generally within theoretical ranges, while identifying low-altitude lake flights as an important scope concern.
- Conclusion: Lower-altitude flights showed more severe large-scale fading, with negative path loss exponents and higher shadowing variability, even over the lake.The conclusion reports both path loss and shadowing across three environments.
- Conclusion: The KS test identified the Weibull distribution as the best description of small-scale fading at a 95% significance level.The study compared theoretical and empirical CDFs of collected fading samples.
- Conclusion: LCRs were used to estimate Doppler frequency and drone speed, with expected Doppler results observed within the theoretical range despite wind-affected average speed.The lake flight at 3 km/h and 8 m provided better discrimination between speed and Doppler estimates.
- Scope and limitation: Low-altitude flights near water produced a high path loss exponent that may hinder applications, motivating further study of water-surface effects on UAV channels.The authors identify this as an area with limited prior work.
Appendix A.1
The appendix characterizes the XBee antenna pattern and its gain composition for the base station and drone antennas. Gain is highest horizontally and lowest vertically, with geometry relative to the BS affecting the effective gain.
- Antenna radiation pattern: The XBee antenna has its highest gain on the horizontal axis and its lowest gain on the vertical axis.The antenna lobe was simulated with 4nec2 software.
- Antenna gain composition: When the UAV is high and close to the BS, overlapping antennas provide low gains; horizontal separation changes the main Fresnel zone and increases gain.The appendix links gain composition to the relative geometry of the BS and drone antennas.