Air-to-Ground Channel Measurement and Modeling for Low-Altitude UAVs: A Survey

doi:10.12142/ZTECOM.202502007

ZTE Communications ›› 2025, Vol. 23 ›› Issue (2): 60-75.DOI: 10.12142/ZTECOM.202502007

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Air-to-Ground Channel Measurement and Modeling for Low-Altitude UAVs: A Survey

CHEN Peng, LIU Yajuan, WEI Wentong, WANG Wei(), LI Na

School of Information Engineering, Chang’an University, Xi’an 710064, China

Received:2025-02-20 Online:2025-06-25 Published:2025-06-10
About author:CHEN Peng graduated from the School of Marine Science and Technology, Northwestern Polytechnical University, Chi
LIU Yajuan received her BS degree in information and communication engineering from Tiangong University, China in 2023. She is currently pursuing a master's degree at Chang'an University, China, focusing on the research of coherent doppler wind lidar research at the School of Information Engineering. Her research interests include optical signal processing, lidar-based atmospheric monitoring, and computational methods for wind profiling.
WEI Wentong received his bachelor's degree in communication engineering from Zhejiang Sci-Tech University, China in 2023. He is currently pursuing a master's degree in information and communication engineering at Chang'an University, China. His research interests include wireless communication, channel modeling, and signal processing, with a focus on channel characteristics, particularly in the context of wireless communication systems. He has presented his research work on channel modeling and analysis at the International Symposium on Antennas and Propagation (ISAPE).
WANG Wei (wei.wang@chd.edu.cn) received his bachelor's degree in communications engineering from Wuhan University, China in 2003, master's degree from the University of Kiel, Germany in 2006, and doctoral degree (summa cum laude) from the University of Erlangen-Nuremberg, Germany in 2014. From 2007 to 2018, he was a scientific staff member with the Institute of Communications and Navigation, German Aerospace Center (DLR). He is currently a professor with the School of Information Engineering, Chang'an University, China. His current research interests include time-variant parameter estimation, channel modeling for localization and navigation, channel measurements, terrestrial radio-based positioning/navigation, and related topics. He received the Best Presentation Paper Award in ION GNSS 2012 and the Best Paper Award in EUCAP 2018 and COTA 2018. He is an associate editor of the IET Microwave, Antennas and Propagation. He served as a TPC Member for ICC and VTC conferences and workshops.
na in 2013, majoring in information countermeasures technology. He received a PhD degree in underwater acoustic engineering also from Northwestern Polytechnical University in 2019, supervised by Academician MA Yuanliang, a renowned expert in airborne sonar. He then joined Chang'an University for postdoctoral research on civil engineering integrated with advanced acoustic/optical sensing at its wind tunnel laboratory. His research interests include low-altitude drones, marine channels, and highway bridge monitoring, integrating multidisciplinary technologies such as acoustics, radio frequency, optics, and fiber-optic sensing. He has published over 10 SCI/EI papers as the first author in top journals (e.g., IEEE TVT, IEEE SPL) and led/participated in multiple national/provincial research projects, including the National Natural Science Foundation of China and the China Postdoctoral Science Foundation.
LI Na received her master's degree in information and communication engineering from Chang'an University, China in 2024. Her research interests include wireless communication, channel modeling, and UAV communication systems, building on her master's thesis in UAV air-to-ground channel analysis and modeling in campus scenes.
Supported by:
the National Natural Science Foundation of China(42176190);Fundamental Research Funds for the Central Universities, CHD(300102243401);Research Funds for the Interdisciplinary Projects, CHU(300104240912)

Abstract

Abstract:

As important infrastructure for airborne communication platforms, unmanned aerial vehicles (UAVs) are expected to become a key part of 6G wireless networks. Thus, modeling low- and medium-altitude propagation channels has attracted much attention. Air-to-ground (A2G) propagation channel models vary in different scenarios, requiring accurate models for designing and evaluating UAV communication links. Unlike terrestrial models, A2G channel models lack detailed investigation. Therefore, this paper provides an overview of existing A2G channel measurement campaigns, different types of A2G channel models for various environments, and future research directions for UAV air-land channel modeling. This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights non-suburban scenarios requiring consideration in future modeling efforts.

Key words: unmanned aerial vehicle (UAV), air-to-ground (A2G), channel measurement, channel modeling, small-scale and large-scale fading

CHEN Peng, LIU Yajuan, WEI Wentong, WANG Wei, LI Na. Air-to-Ground Channel Measurement and Modeling for Low-Altitude UAVs: A Survey[J]. ZTE Communications, 2025, 23(2): 60-75.

Figures/Tables 11

Table 1 Research on large-scale A2G propagation and its path loss parameters in existing literature

Ref.	Scenario	Propagation Path	Model	PLE $γ$
[13]	Urban/suburban	LoS	Log-distance path loss model, two-ray model	L-band: 1.7, C-band: 1.5–2
[14]	Urban/open field	LoS	Free space path loss model
[15]	Urban/rural	LoS	Log-distance path loss model	4.1
[16]	Open field	LoS	Log-distance path loss model	2.01
[17]	Aerial	LoS	Log-distance path loss model	2.32
[18]	Water	LoS	Log-distance path loss model	1.9
[19]	Urban/suburban	LoS, NLoS	Free space path loss model
[20]	Urban	LoS, NLoS	Free space path loss model

Table 1 Research on large-scale A2G propagation and its path loss parameters in existing literature

Ref.	Scenario	Propagation Path	Model	PLE $γ$
[13]	Urban/suburban	LoS	Log-distance path loss model, two-ray model	L-band: 1.7, C-band: 1.5–2
[14]	Urban/open field	LoS	Free space path loss model
[15]	Urban/rural	LoS	Log-distance path loss model	4.1
[16]	Open field	LoS	Log-distance path loss model	2.01
[17]	Aerial	LoS	Log-distance path loss model	2.32
[18]	Water	LoS	Log-distance path loss model	1.9
[19]	Urban/suburban	LoS, NLoS	Free space path loss model
[20]	Urban	LoS, NLoS	Free space path loss model

Table 2 Fading characteristics of small-scale air-to-ground propagation channels in the literature

Ref.	Frequency/GHz	Fading Distribution	K-factor/dB	Scenario
[33]	3.1–5.3	Nakagami		Suburban/open field
[15]	2	Rayleigh, Ricean		Urban/suburban
[30]	5.75	Ricean	-5–10	Urban/suburban
[32]	0.968–2.06	Ricean	12–27.4	Urban/suburban
[34]	8–18	Ricean, Nakagami	2–5	Forest
[31]	2	Ricean		Urban/suburban

Figure 1 Doppler shifts in different states of the aircraft

Figure 2 A typical scenario of UAV air-to-ground channel propagation

Table 4 Comparison of geometry-based stochastic channel models

Model

Ref.

Angle Distribution Function

Scenario

Cylindroid

[27, 62]

AOA: $f_{α_{r x, n, m}^{- s}} (α) = \frac{e^{k c o s (α - α_{r x, n}^{- s})}}{2 π I_{0} (k)}$

EOA: $f_{β_{t x, n, m}^{- S}} (β) = \frac{π}{4 |β_{R_{m}}|} c o s (\frac{π}{2} \frac{β - β_{t x, n, m}^{- S}}{β_{R_{m}}})$

Highly accurate, but relies on geographic information and high computational complexity

Sphere

[61–63]

AOA:

P_{α_{r x, n, m}, β_{r x, n, m}}^{- S} (α, β) = \frac{e^{k (c o s β c o s β_{r x, n}^{- S} c o s (α - α_{r x, n}^{- S}) + s i n β s i n β_{r x, n}^{- S})}}{(k^{- \frac{1}{2}}) {(2 π)}^{\frac{3}{2}} I_{\frac{1}{2}} (k)}

Angular parameters can be abstracted to specific mathematical distributions, which can greatly simplify calculations

Cylinder

[57] (R=3 km, $H_{C}$ =300 m);

[64] (R=100 m);

[44] (R=500 m);

[65] (R=50 m, $H_{C}$ =700 m)

AOA: $f_{α_{r x, n, m}^{- s}} (α) = \frac{e^{k c o s (α - α_{r x, n}^{s})}}{2 π I_{0} (k)}$

EOA: $f_{β_{t x, n, m}^{- s}} (β) = \frac{π}{4 | β_{R_{m}} |} c o s (\frac{π}{2} \frac{β - β_{t x, n, m}^{- s}}{β_{R_{m}}})$

Table 4 Comparison of geometry-based stochastic channel models

Model

Ref.

Angle Distribution Function

Scenario

Cylindroid

[27, 62]

AOA: $f_{α_{r x, n, m}^{- s}} (α) = \frac{e^{k c o s (α - α_{r x, n}^{- s})}}{2 π I_{0} (k)}$

EOA: $f_{β_{t x, n, m}^{- S}} (β) = \frac{π}{4 |β_{R_{m}}|} c o s (\frac{π}{2} \frac{β - β_{t x, n, m}^{- S}}{β_{R_{m}}})$

Highly accurate, but relies on geographic information and high computational complexity

Sphere

[61–63]

AOA:

P_{α_{r x, n, m}, β_{r x, n, m}}^{- S} (α, β) = \frac{e^{k (c o s β c o s β_{r x, n}^{- S} c o s (α - α_{r x, n}^{- S}) + s i n β s i n β_{r x, n}^{- S})}}{(k^{- \frac{1}{2}}) {(2 π)}^{\frac{3}{2}} I_{\frac{1}{2}} (k)}

Angular parameters can be abstracted to specific mathematical distributions, which can greatly simplify calculations

Cylinder

[57] (R=3 km, $H_{C}$ =300 m);

[64] (R=100 m);

[44] (R=500 m);

[65] (R=50 m, $H_{C}$ =700 m)

AOA: $f_{α_{r x, n, m}^{- s}} (α) = \frac{e^{k c o s (α - α_{r x, n}^{s})}}{2 π I_{0} (k)}$

EOA: $f_{β_{t x, n, m}^{- s}} (β) = \frac{π}{4 | β_{R_{m}} |} c o s (\frac{π}{2} \frac{β - β_{t x, n, m}^{- s}}{β_{R_{m}}})$

Figure 3 MIMO air-to-ground channel model of a UAV

Figure 4 Two-ray model

Table 5 Two-ray model for selected A2G channels

Ref.	Frequency	Bandwidth	Transmit Power/dBm	Channel Characteristics
[13, 23, 32, 72]	0.968 GHz, 5.06 GHz	5 MHz, 50 MHz	40	PL, K-factor
[15]	2.05 GHz	—	—	MPC, K-factor, PL
[33]	3.1–5.3 GHz	2.2 GHz	-14.5	PL, MPC
[17]	2.4 GHz	—	0	RSSI
[59]	5.7 GHz	—	40	PL
[73–74]	200 MHz–5 GHz	—	—	PL, SF

Table 6 Comparison of the models proposed in Refs. [73–76]

Model	Advantage	Disadvantage
Measurement-based model	Matching actual channel scenarios	Single application scenario
Ray-tracing-based model	Discriminating multipath in the channel	High computational volume and complexity
Geometric random channel model	Matching actual channel scenarios	More complex calculations

Figure 5 Measurement scenarios and equipment

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Air-to-Ground Channel Measurement and Modeling for Low-Altitude UAVs: A Survey

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