1 Channel Modeling for UA V-to-Ground Communications with Posture Variation and

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Channel Modeling for UAV-to-Ground

Communications with Posture Variation and

Fuselage Scattering Effect

Boyu Hua, Member, IEEE, Haoran Ni, Qiuming Zhu*, Member, IEEE,

Cheng-Xiang Wang*, Fellow, IEEE, Tongtong Zhou, Kai Mao, Student

Member, IEEE, Junwei Bao, Xiaofei Zhang, Member, IEEE

Abstract

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role in reliable

communications between UAV and ground terminal. This paper proposes a three-dimensional (3D) non-

stationary hybrid model including large-scale and small-scale fading for U2G multiple-input-multiple-

output (MIMO) channels. Distinctive channel characteristics under U2G scenarios, i.e., 3D trajectory and

posture of UAV, fuselage scattering effect (FSE), and posture variation fading (PVF) are incorporated

into the proposed model. The channel parameters, i.e., path loss (PL), shadow fading (SF), path delay,

and path angle, are generated incorporating machine learning (ML) and ray tracing (RT) techniques

to capture the structure-related characteristics. In order to guarantee the physical continuity of channel

parameters such as Doppler phase and path power, the time evolution methods of inter- and intra-

stationary intervals are proposed. Key statistical properties, including temporal auto-correction function

(ACF), power delay proﬁle (PDP), level crossing rate (LCR), average fading duration (AFD), and

stationary interval (SI), are analyzed with the impact of the change of fuselage and posture variation.

It is demonstrated that both posture variation and fuselage scattering have crucial effects on channel

characteristics. The validity and practicability of the proposed model are veriﬁed by comparing the

simulation results with the measured ones.

This work was supported by National Natural Science Foundation of China, No. 62271250, National Key Scientiﬁc

Instrument and Equipment Development Project, No. 61827801, Key Technologies R&D Program of Jiangsu (Prospective and

Key Technologies for Industry), No. BE2022067 and BE2022067-3, Natural Science Foundation of Jiangsu Province, No.

BK20211182, open research fund of National Mobile Communications Research Laboratory, No. 2022D04, Key Project of

Aeronautical Science Foundation of China, No. 2020Z073009001. (Corresponding author: Qiuming Zhu, Cheng-Xiang Wang)

B. Hua, H. Ni, Q. Zhu, T. Zhou, K. Mao, and X. Zhang are with the Key Laboratory of Dynamic Cognitive System of

Electromagnetic Spectrum Space, College of Electronic and Information Engineering, Nanjing University of Aeronautics and

Astronautics, Nanjing 211106, China (e-mail: byhua; nihaoran; zhuqiuming; zhoutongtong; maokai; zhangxiaofei@nuaa.edu.cn).

C.-X. Wang is with the National Mobile Communications Research Laboratory, School of Information Science and Engineering,

Southeast University, Nanjing 210096, China, and also with the Pervasive Communication Research Center, Purple Mountain

Laboratories, Nanjing 211111, China (e-mail: chxwang@seu.edu.cn).

J. Bao is with the Department of Physics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

(e-mail: broadenway@nuaa.edu.cn).

arXiv:2210.02245v3 [eess.SP] 13 Oct 2022

Index Terms

Channel model, Fuselage scattering, Non-stationary, Posture variation, UAV.

I. INTRODUCTION

With emerging global connectivity requirements, the space-air-ground-sea integrated networks

are proposed to provide seamless coverage, ultra-reliable connection and user diversity [1].

Owing to the low cost, high mobility and versatility, unmanned aerial vehicles (UAVs) have been

considered to be a promising paradigm for establishing air-ground communication networks [2].

Thus, the research of UAV-to-ground (U2G) wireless communication has become an essential

part of the pre-research work of next generation mobile communication systems [3]. Unlike

terrestrial communication nodes, UAVs have unique characteristics, including three-dimensional

(3D) arbitrary trajectory, 3D antenna arrangement, and 3D rotational posture [4]. These new

features would cause different non-stationarity that conventional channel models cannot precisely

present. To better design and evaluate future air-ground communication systems, it is critical to

provide a realistic and reliable U2G channel model [5].

A. Related Works

From the perspective of modeling approaches, modern U2G channels can be modeled by the

statistical method, geometrical method, and machine learning (ML)-enabled method [6]. The

statistical channel models employ mathematical formulas from empirical observations such as

ﬁeld measurements or ray-tracing (RT) simulations. These models are usually employed for link-

level analysis due to the lack of implicit temporal and spatial continuity. The shortcomings of

pure statistical models are solved by incorporating geometrical information, and the improved

models are usually referred to as geometry-based stochastic channel models (GBSMs). In fact,

many standardized channel models utilize the geometrical method assuming different geometry

shapes or unique channel characteristics [7]. By assuming that the scatterers obey diverse 3D

geometric distributions, such as cylinder [8], ellipsoid [9], and sphere [10], GBSMs can support

3D propagation scenarios and become the mainstream method of U2G channel modeling. The

fundamental concept of the ML-enabled method is obtaining the latent channel characteristics

using ML technologies [11]–[13]. However, the ML method can merely provide channel infor-

mation at a particular condition and heavily rely on the training dataset. Therefore, the future

U2G channel modeling may combine various methods for their advantages.

Several U2G channel models were proposed to describe the motion characteristic of UAVs.

For example, a basic 3D GBSM was proposed in [14] to study the U2G channels, which assumed

the ground terminals were ﬁxed. However, that assumption limited the versatility of such kind of

models. Some existing U2G channel models assumed that the UAV or ground terminal moved at

constant speed in a straight line [15]. But in a practical environment, the vehicle may experience

velocity change and arbitrary trajectory. The U2G channel models in [16] considered both the

UAV and vehicle moved with constant velocities, which was inconsistent with common scenes.

A modiﬁed U2G channel model considering the 3D speed variation of UAV was proposed in

[17], but the trajectory of the ground terminal was still ﬁxed. By setting the trajectory of both

UAV and ground terminal, authors in [18] proposed a more realistic dual-mobile UAV channel

model and studied the corresponding simulations. Moreover, the authors in [19] proposed a

U2G multiple-input multiple-output (MIMO) channel model, and the transceivers moved along

3D trajectories.

On the other hand, U2G channels have obvious non-stationary characteristics when UAVs

move rapidly in 3D space. The researchers upgraded the stationary channel models by introducing

time-variant channel parameters. For example, authors in [20] introduced time-varying departure

and arrival angle parameters into the concentric cylindrical channel model, and the modiﬁed

model can reﬂect the non-stationary characteristics of the UAV channel. A non-stationary MIMO

U2G channel model was proposed in [21], where the distribution of time-variant scatterers was

improved to support the non-stationarity. To describe the non-stationary effect, authors in [22]

employed the geometric relationship between the sphere and truncated ellipsoid in the U2G

channel model to update the time-variant channel parameters dynamically.

B. Motivations

The aforementioned U2G channel models are not realistic enough because they merely focus

on the motion characteristic of UAVs. However, some other characteristics of UAV, such as

posture rotation and fuselage scattering, are also pivotal [23]. The drone pitch rotation and

its impact on the channel were studied in [24], thus, the model can describe the basic posture

rotation. Further, the effects of three posture angles and fuselage scattering were discussed in [25],

and a modiﬁed U2G channel model was proposed. Besides, a U2G channel model considering

the visible region of the UAV antenna was proposed in [26], and the birth-death procedure

of the local multi-path components was analyzed. The authors in [27] combined the channel

model with the airborne random motion model, where the UAV trajectory related to time-variant

heading was modiﬁed. A U2G channel model with dynamic space-time cluster parameters was

proposed in [28], and the space-time clusters were analyzed in the antenna array domain and time

domain. The RT method was utilized to achieve a more realistic channel description in [29], and

a U2G channel model considering antenna beamforming was established. These concepts, which

incorporate more deterministic information into U2G channel modeling, will also be applied in

this paper to ensure the authenticity of U2G channel modeling.

Meanwhile, it should be noticed that some channel parameters, e.g., the Doppler frequency

shift and path power, in non-stationary models are not realistic enough. For example, authors in

[30], [31] deﬁned the time-variant Doppler frequency shift instead of the constant one. However,

it leads to physically inconsistent phase variations in the channel model [32]. The concept of

calculating Doppler frequency by the integral method was mentioned in [33] to get a more

realistic phase variation. The path power parameter was discussed in [34], where the time-

variant delays were employed to calculate the power for each sub-path. Moreover, authors in

[35] pointed out that the path phase should be determined by total path length to capture the

Doppler effect caused by dual-mobility, and a squared sine function could model the path power

ramp. However, a thorough channel parameter generation procedure is still absent for establishing

a more realistic U2G non-stationary channel model.

The limitations mentioned above suggest a requirement to develop a non-stationary U2G

channel model considering the unique characteristics of UAVs with more realistic channel

parameter generation and time-evolution. This paper aims to ﬁll these research gaps.

C. Contributions

Motivated by the above background and current research gaps, this paper proposes a realistic

hybrid channel model combining the geometrical method, RT approach, and ML technology

to consider the deterministic information and unique characteristics of the U2G communication

scenario. The contributions and novelties of this article include the following.

1) A non-stationary wideband U2G MIMO channel model is presented by thoroughly con-

sidering the impact of UAV posture variation and fuselage scattering effect (FSE). The model

describes both large-scale and small-scale fading in three segments, i.e., near-UAV segment

(NUS), free space link (FSL), and near-ground segment (NGS). The posture variation fading

(PVF) coefﬁcient and the deterministic scattering caused by FSE are considered. Based on these

improvements, the realistic U2G channel can be characterized by the proposed model.

2) Generation and time-evolution procedure of segmented time-variant channel parameters,

including the path loss (PL), shadow fading (SF), delay, power, angle, and phase, are given. The

RT method and ML technology are employed to modify the traditional empirical and geometry-

based parameter calculation method. By introducing the jerk-limited function to describe the

path power ramp and utilizing the propagation distance variation to capture the Doppler effect,

the time-evolution continuity of the channel parameters is achieved, which describes the channel

non-stationarity more accurately.

3) Statistical properties of the proposed U2G channel model are given and simulated. The

simulated channel characteristics, including temporal auto-correction function (ACF), power

delay proﬁle (PDP), level crossing rate (LCR), average fading duration (AFD), and stationary

interval (SI) are analyzed and compared with results from other channel models, which veriﬁes

the effectiveness of the proposed model.

4) The practicability of the proposed model is validated by measurement data. Measured results

from the measurement campaigns or referred literature are compared with simulation results of

the proposed model, including PL, ACF, SI, and PVF coefﬁcient. Moreover, the PL, ACF and

PVF coefﬁcient show that UAV posture variation and fuselage scattering have vital impacts on

channel characteristics.

The remainder of this paper is organized as follows. Section II describes a 3D non-stationary

U2G channel model incorporating posture variation and FSE. Section III studies the generation

and time-evolution of the model parameters. Statistical properties of proposed model are pre-

sented and analyzed in Section IV. Section V compares and discusses the analytical, simulated,

and measured results. Conclusions are ﬁnally drawn in Section VI.

II. CHANNEL MODEL INCORPORATING POSTURE VARIATION AND FSE

A typical U2G communication scenario is shown in Fig. 1. The transmitter (Tx) on the UAV

equips with Pantenna elements, and the receiver (Rx) on the ground vehicle equips with Q

antenna elements. Note that ex-ey-ezand x-y-zare two independent coordinate systems for Tx

and Rx, respectively. The origin position of the coordinate system is located on the ﬁrst element

of the equipped antenna array. The communication channel consists of the line-of-sight (LoS)

component and non-LoS (NLoS) components. Moreover, posture angles for pitch, roll, and yaw

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1ChannelModelingforUAV-to-GroundCommunicationswithPostureVariationandFuselageScatteringEffectBoyuHua,Member,IEEE,HaoranNi,QiumingZhu*,Member,IEEE,Cheng-XiangWang*,Fellow,IEEE,TongtongZhou,KaiMao,StudentMember,IEEE,JunweiBao,XiaofeiZhang,Member,IEEEAbstractUnmannedaerialvehicle(UAV)-to-ground(U2G)cha...

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