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Characterizing the 80 GHz Channel in
Static Scenarios: Diffuse Reflection,
Scattering and Transmission through
Trees under Varying Weather Conditions
RADEK ZAVORKA1, TOMAS MIKULASEK1, JOSEF VYCHODIL1, JIRI BLUMENSTEIN1,
ANIRUDDHA CHANDRA2, HUSSEIN HAMMOUD3, JAN MARCIN KELNER4,CEZARY HENRYK
ZIÓłKOWSKI4, THOMAS ZEMEN5, CHRISTOPH MECKLENBRÄUKER6, ALES PROKES1.
1Department of Radio Electronics, Brno University of Technology, Brno, Czech Republic
2National Institute of Technology, Durgapur, India
3University of Southern California, Los Angeles, USA
4Institute of Communications Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland
5AIT Austrian Institute of Technology GmbH, Vienna 1210, Austria
6Institute of Telecommunications, TU Wien, Vienna, Austria
Corresponding author: Radek Zavorka (e-mail: xzavor03@vutbr.cz).
The research described in this paper was financed by the Czech Science Foundation, Project No. 23-04304L, Multi-band prediction of
millimeter-wave propagation effects for dynamic and fixed scenarios in rugged time varying environments and by the Internal Grant
Agency of the Brno University of Technology under project no. FEKT-S-23-8191. The work of A. Chandra is supported by the
Chips-to-Startup (C2S) program no. EE-9/2/2021-R&D-E from MeitY, GoI. The work of Jan M. Kelner and Cezary H. Ziółkowski was
funded by the National Science Centre, Poland, under the OPUS-22 (LAP) call in the Weave program, as part of research project no.
2021/43/I/ST7/03294, acronym ‘MubaMilWave’. The work of Thomas Zemen is funded within the Principal Scientist grant Dependable
Wireless 6G Communication Systems (DEDICATE 6G) at the AIT Austrian Institute of Technology.
ABSTRACT The deployment of wireless systems in millimeter wave relies on a thorough understanding
of electromagnetic wave propagation under various weather conditions and scenarios. In this study,
we characterize millimeter wave propagation effects from measurement data, utilizing channel impulse
response analysis with a focus on root mean square delay spread and Rician K-factor. The obtained
results highlight the significant influence of weather conditions and foliage on propagation, including
diffuse reflection, scattering, and absorption. Particularly, we observed a notable increase in scattering from
deciduous trees with leaves, in comparison with bare trees or ones covered by snow or ice. The attenuation of
the signal propagated through a tree with foliage is 2.16 dB/m higher compared to a bare tree. Our validation
measurements within a semi-anechoic chamber confirmed these observations and aided in quantifying the
differences. These findings offer valuable insights into the dynamics of millimeter-wave signals that are
important for advancing wireless communication technologies.
INDEX TERMS Channel impulse response, Weather conditions, RMS delay spread, Rician K-factor,
Channel characterization, 80 GHz channel sounding, Channel modeling
I. INTRODUCTION
THE deployment of wireless communication systems in
the millimeter wave (MMW) frequency band has be-
come increasingly popular in recent years due to its abundant
available bandwidth, which enables high data rates. The
MMW frequency band offers several advantages such as low
interference [1], [2], and the potential for multi-gigabit data
rates [3]. However, the use of MMW frequencies for wireless
communication systems faces challenges such as severe path
loss, signal blockage, and high atmospheric absorption [4],
especially in non-line-of-sight (NLOS) conditions. There-
fore, understanding the propagation characteristics of MMW
channels is essential for designing and developing efficient
communication systems.
The propagation characteristics of MMW channels have
been extensively studied in recent years, with a particular
focus on the measurement of the channel impulse response
(CIR) [5]. The CIR represents the response of the channel to
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an Dirac impulse and provides information on the channel’s
multipath characteristics, such as delay and Doppler spread.
The channel characteristics obtained from CIR measure-
ments are crucial for designing communication systems that
are robust to the different propagation environments.
An analysis of the influence of different seasons and
weather on signal propagation in the vicinity of the tree
was conducted. For the description, parameters widely used
in radio channel characterization, such as root mean square
(RMS) delay spread, Rician Kfactor, and path loss, were
chosen because of their significance in defining radio chan-
nels [6].
A. LITERATURE REVIEW
The E-band, which includes frequencies from 71-76 GHz and
81-86 GHz, contains two extremely broad consecutive 5 GHz
bandwidths, allowing for data rates of tens of gigabits per
second. The focus is placed on the E-band due to its potential
for a wide range of applications such as 5G and 6G backhaul
[7], point-to-point wireless links, vehicle-to-vehicle (V2V)
[8] or vehicle-to-infrastructure (V2I) communication, vehicle
radar applications [9], [10], and satellite-to-satellite [11] or
ground-to-satellite [12] communication. When propagated
through the atmosphere, the E-band wave is less susceptible
to oxygen absorption compared to the 60 GHz band [13]. It is
essential to employ highly directed, high-gain antennas [14].
Several measurement campaigns have been conducted to
investigate the propagation characteristics of MMW channels
in various scenarios. These scenarios include indoor [15],
outdoor [16], and vehicular environments [17]. In indoor sce-
narios, researchers have investigated the propagation charac-
teristics of MMW channels in different rooms and buildings,
considering the impact of walls, ceilings, and furniture on the
channel characteristics. The study [18] described specifica-
tions of outdoor scenarios, exploring how various obstacles
like buildings, trees, and vegetation affect the propagation of
MMW signals. In vehicular settings, the propagation char-
acteristics of MMW channels within moving vehicles were
explored, considering aspects like Doppler shift, shadowing
effects, and delay spread [19].
Studies have shown that trees, especially deciduous trees,
can cause significant signal attenuation due to the high wa-
ter content in leaves [20] and branches, which absorb and
scatter the electromagnetic waves. Moreover, foliage and
branches can cause significant multipath diffuse reflections
and scattering, leading to additional path loss and distortion
in the received signal. In [21] researchers analyze foliage
attenuation of cherry tree and it was determined to 0.4 dB/m
for both co- and cross- polarized antenna configurations.
In [22] researchers discovered that the attenuation caused
by trees at 28 GHz was between 16-18 dB without leaves, and
aproximately 10 dB higher with leaves present. Foliage loss
at 28 GHz for a specific antenna configuration is analyzed
in [23], which indicates an attenuation range of 10–40 dB
depending on the antenna’s position. Similarly, [24] deter-
mined a vegetation-dependent attenuation factor by compar-
ing the mean received power for line-of-sight (LOS) and
NLOS links, which ranged from 19 dB to 26 dB at 28 GHz,
depending on the polarization of the transmitted signal. Study
[25] indicates that the effect of trees on signal attenuation
during measurement reached up to 30 dB, depending on the
part of the treetop.
In forest environments, the signal attenuation and path loss
are even more pronounced due to the complex and irregular
nature of the tree canopy and ground terrain. Several studies
have explored the effect of different forest types and densities
on radio signal propagation, and have proposed models to
predict the attenuation and path loss in such environments
[26]–[29].The effect of foliage up to 100 GHz was studied
by [30], [31] and leaf state was taken into account in the
COST 235 report [32, Section 4.2.3.5]. Nevertheless, the
focus of these studies was to consider foliage as an additional
blockage that affects path loss. Studies till 60 GHz were made
in [33]–[36], while in [37], the authors considered the effect
of polarization. Recently, the signal attenuation due to foliage
in the D-band (110-170 GHz) was investigated [38]. These
studies have highlighted the need to carefully consider the
effect of trees and forests on radio signal propagation when
designing and deploying wireless communication systems,
particularly in outdoor and rural environments.
B. CONTRIBUTION OF THIS PAPER
In this paper, we present our measurements of the static
CIR in the frequency band of 80 GHz with a bandwidth of
2.048 GHz. Our objective was to investigate the variations in
the radio channel, with a focus on the impact of vegetation
and various weather conditions. Specifically, we decided to
investigate the influence of trees, which are common in
urban areas. To minimize the impact of other interfering
objects, we opted for an open space environment. Because
for regions of Central and Northern Europe, Northern Asia,
and North America, the characteristic feature is the change
of seasons, making it crucial to understand the channel’s
characteristics under various weather conditions including
snow, ice and trees with or without foliage. The measure-
ments were carried out in an outdoor scenario, where we
measured the scattering of radio signals from a deciduous tree
that is located in the center of a meadow. Additionally, we
measured the signal propagation through the trees to analyze
the effects of vegetation and various weather conditions on
the MMW channel. The measurement campaign utilized a
MMW channel sounder with an open-ended waveguide an-
tenna (OWGA)/horn antenna, positioned at various locations
with respect to the tree to obtain a better understanding
of the propagation characteristics of the MMW channel.
The measurement results were analyzed to extract channel
characteristics such as RMS delay spread or path loss, which
is a measure of the time dispersion of the received signal, and
the power of the different multipath components (MPC).
The main contributions of this paper are as follows:
•We present a measurement campaign of a static channel
in the frequency band of 80 GHz in an outdoor scenario
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for various weather conditions (winter, spring and sum-
mer).
•We analyze the impact of a deciduous tree on the propa-
gation of MMW signals, considering the scattering and
transmission through the tree.
•We extract the channel characteristics such as RMS
delay spread, Rician Kfactor and the power of the dif-
ferent MPC and discuss their implications for designing
and developing efficient communication systems.
C. ORGANIZATION OF THIS PAPER
The organization of the remaining sections in this paper
is as follows: Section II provides a detailed description of
the measured scenarios, emphasizing their significance. Sec-
tion III provides information about measurement setup III-A,
data calibration and preprocessing III-B and subsection III-C
discusses crosstalk elimination between transmitter (TX) and
receiver (RX) using an OWGA. In Section IV, we analyze the
channel through CIR measurement data and analyze path loss
model, RMS delay spread and Rician Kfactor. Section V
validates the results in an semi-anechoic chamber. Finally,
in Section VI, we summarize the results and outline future
research directions.
II. DESCRIPTION OF MEASURED SCENARIOS
Selecting the appropriate tree for year-round measurements
in varying weather conditions is very important. Considering
factors such as the surrounding obstacles, it is crucial to con-
sistently employ the same transmitter and receiver positions
for the purpose of comparing and evaluating the measured
results. The method for describing these precise positions
will be provided later.
Measuring Scenario is defined by a single deciduous tree,
specifically a willow (Salix caprea), with a diameter of
approximately 3 meters, situated in the northern part of
the Orlické Mountains in the Czech Republic. The global
positioning system (GPS) coordinates are N 50°19.52265’,
E 16°23.22580’. Aerial drone photography of the measure-
ment points and the landscape from a bird’s-eye view is
presented in Fig. 1. We utilized reflective sport cones to indi-
cate the positions of the transmission and reception points.
For enhanced visualization, Fig. 2 reflects the mentioned
scenarios and includes all distances, while Tab. 1 summarizes
tree dimensions.
TABLE 1. Summary of trees parameters
Tree 1 Tree 2
Height [m] 4.3 3.7
Diameter [m] 3 2.5
Over several months, we conducted measurements for the
same scenario under a range of weather conditions, including
snowy, icy, snow-free without foliage, and snow-free with fo-
liage conditions. These sub-scenarios are labeled as ’SNOW,’
’ICY,’ ’NO-SNOW, NO-FOLIAGE,’ and ’FOLIAGE.’ We
Tree 1
Tree 2
1
2
3
4
5
FIGURE 1. Drone photograph capturing measurement points and the
landscape from a bird’s eye view, taken at the end of winter for the ’ICY’
sub-scenario
h=1.043 h=0.962
h=0.935
h=0.91
h=0.92
Reflection
105
o
145o
180o
Deciduous
tree1
Deciduoustree2
Legend:
alldistancesareinmeters
histheheightoftheantennas
abovetheground
h=0.965
Trench
Legend:
alldistancesareinmeters
histheheightofthe
antennasabovetheground
h=1.11
Stump
Nail
h=1.0
FIGURE 2. Diagram of measurement scenarios with distances
maintained a fixed position for the transmitter while re-
locating the receiver. Our measurements encompassed the
following:
•Examination of the scattering by the tree.
•Investigation of transmission through the tree with a
180-degree angle between the TX and RX.
•Adjustment of the receiver angles to approx. 145 and
105 degrees relative to the transmitter.
•Execution of measurements through two trees, as de-
picted in Figs. 1 and 2, with the antennas orientations
also documented.
For the sake of achieving utmost measurement precision
and repeatability, we adopted a systematic approach. During
the initial measurement, we strategically marked the TX and
RX locations by embedding metal stakes securely into the
ground, as depicted in Fig. 3. Throughout all subsequent
measurements, we meticulously adhered to the prescribed
positions of the transmitting and receiving antennas, con-
sidering their height, distance and direction relative to the
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measurement object. This rigorous protocol ensured that all
measurements, conducted under varying weather conditions,
were consistently acquired from the exact same position.
Fig. 4 shows photographs taken during year-round measure-
ments capturing the varying weather conditions and details
of an icy branch and a springtime branch.
Metal stick as a reference
point for repetitive
measurement
Plumb bob
Waveguide &
horn antenna
FIGURE 3. Point marking for repeated measurement
March
2023
April
2023 May
2023
June
2023
Summer
Foliage
Winter
Snow on
tree/ground
Late Winter
Icy
tree/ground
Spring
No snow
No foliage
FIGURE 4. Photos of all sub-scenarios for scattering measurement
III. MEASUREMENT SETUP
A. EQUIPMENT AND CONFIGURATION
The measurement setup schematic is depicted in Fig. 5. The
Xilinx Zynq UltraScale+ RFSoC ZCU111 board is utilized
as a transmit baseband subsystem. Its fast DACs, clocked at
6.144 GSPS, are used to generate an intermediate frequency
signal in the form of its I and Q components. Although
the system is capable of dealing with complicated signals,
a simple frequency modulated continuous wave (FMCW)
signal with ramp-up and ramp-down in order to make the
spectrum as flat as possible was chosen as the excitation
signal. The FMCW signal is very robust when non-linearities
are present in the system which was the major reason for this
choice. The FMCW bandwidth is B= 2.048 GHz and its
duration is T= 8 µs, which enables fast measurements up to
fmeas =1
T= 125 kHz (measurements per seconds), while
still maintaining reasonable signal to noise ratio (SNR). In
the case of static scenarios, the SNR can be further improved
by averaging.
The signal is upconverted to the desired millimeter wave
frequency by the Sivers IMA FC1003E/03 up/down con-
verter. The Agilent 83752A generator is used to provide
a frequency stable, low phase noise local oscillator signal
for the upconversion. A Datum LPRO 10 MHz rubidium
oscillator is used to generate the reference frequency. The
RF signal power is boosted by the Filtronic Cerus 4 AA015
power amplifier (PA) and transmitted using an OWGA or a
25dBi horn antenna, whose radiation patterns are depicted
in Fig. 6 and details are noted in Tab. 2.
TABLE 2. Parameters of OWGA and horn antennas
OWGA Horn
Gain [dBi] 7 24.8
HPBW [°] 52 10.5
The signal is propagated through the measured environ-
ment and is then received by a processing chain analo-
gous to the transmitting side of the measurement setup.
The signal is received by the same type of waveguide or
horn antenna and amplified by the Low noise factory LNF-
LNR55_96WA_SV low noise amplifier (LNA). The down-
conversion is performed by the Sivers IMA FC1003E/02
up/down converter with a local oscillator signal generated by
the Agilent E8257D generator. The signal is finally sampled
at the intermediate frequency in the form of its I and Q
components by the fast ADCs (clocked at 4.096 GSPS) of an
other Xilinx Zynq UltraScale+ RFSoC ZCU111 board and
saved to the SSD for further processing.
The frequency plan is depicted in Fig. 7. The complex
baseband FMCW signal with a bandwidth of 2.048 GHz is
digitally shifted by 1.024 GHz to create an upper sideband
(USB) signal for transmission. This signal is then upcon-
verted with a carrier frequency of ftx = 81.600 GHz.
The converters have a poor image rejection ratio (IRR) of
20-40 dB, which is partially compensated, but still results in
an out-of-band transmission, but this is not a concern in those
experiments.
The signal is received and downconverted with a carrier
frequency of frx = 83.648 GHz. This time the useful signal
is contained in the lower sideband (LSB). The signal is
sampled, digitally shifted by 1.024 GHz and stored for further
processing. Again, as the converters have a poor IRR a
partially attenuated image is received, but this is also not a
concern as there is minimal traffic at those frequencies.
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ZCU111
tx
rx
13.6000 GHz
Agilent 83752A
20 dBm
Low Noise Factory
LNFLNR55_96WA_SV
81.6-83.6 GHz
MIXER
f × 6
Input I
Input Q
MMW
TX
Siversima
FC1003E PC
13.94133 GHz
Agilent E8257D
22 dBm
Horn
antenna
Transmitter Receiver
MIXER
f× 6
Output I
Output Q
MMW
RX
Siversima
FC1003E
GPS/
Rubidium
oscillator
Cooler
PA
Filtronic
Cerus 4 AA015
10 MHz Reference
3 dB
attenuator
LNA
Horn
antenna
ZCU111
tx
rx
GPS/
Rubidium
oscillator
10 MHz Reference
EthernetEthernet
or open
waveguide
or open
waveguide
FIGURE 5. Measurement system schematic
-200 -150 -100 -50 0 50 100 150 200
Theta [deg]
-60
-50
-40
-30
-20
-10
0
10
20
30
Gain [dBi]
10.5
52
Open-ended: E-plane
Open-ended: H-plane
Horn: E-plane
Horn: H-plane
FIGURE 6. Simulated radiation pattern of OWGA and horn antennas at
82.6 GHz
79 80 81 82 83 84 85 86
f [GHz]
0
10
20
30
40
50
P
TX image
TX/RX
RX image
RX LO
TX LO leakage
FIGURE 7. Measurement system frequency plan
B. SYSTEM CALIBRATION AND DATA
PREPROCESSING
In order to calibrate the system for a flat frequency response
and stable phase, the transmitter and receiver are connected
directly via a 79 dB attenuator (ATT). After all of the in-
struments are thermally stabilized, calibration (reference) I/Q
data are taken. Then they are used for the calibration as
mentioned below. To enhance the SNR of those data, 1024
snapshots are averaged.
The preprocessing of the received FMCW data to obtain
an estimate of the CIR is done as follows. In the context of
the pseudocode part below, lowercase variables will represent
time domain signals and UPPERCASE variables signals in
the frequency domain.
•First of all both the received I/Q data and cal-
ibration I/Q data are transformed into the fre-
quency domain via fast fourier transform (FFT),
e.g.: DATA(f) = FFT{data(t)}and DATACAL(f) =
FFT{datacal(t)}.
•Most of the energy of the signal is within the range
of ±980 MHz, so only this data are used for further
processing: DATA(f) = DATA(−980:980 MHz) and
DATACAL(f) = DATACAL(−980:980 MHz).
•The data are divided by the calibration data in the fre-
quency domain: DATA(f) = DATA(f)/DATACAL(f).
This will provide the flat frequency response of the
system and consistent phase characteristics as well as
the pulse compression processing.
•A Blackman window is applied in order to re-
duce the spectral leakage: DATA(f) = DATA(f)·
BLACKMAN(f).
•The data are transformed back into the time domain:
data(t) = IFFT{DATA(f)}.
•The data are circularly shifted to ensure that the maxi-
mum peak is at the center of the dataset. This is done to
facilitate easier data manipulation.
•Only the central quarter of the data is selected,
as this region contains the most relevant informa-
tion, while the remaining portions may contain un-
wanted artifacts or spurious signals, which arise
mainly from the non-linearities present in the sys-
tem. Note that the signal length corresponds to
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d=t·c= 8 µs·3·108m/s = 2400 m. A quarter of
this is 600 m. As the strongest peak is located in the
middle of the signal, we can still observe up to 300 m
of MPCs which is sufficient for short-distance commu-
nication channels of approximately up to 100 meters.
C. CROSSTALK ELIMINATION BETWEEN TX AND RX
OPEN-ENDED WAVEGUIDE ANTENNA
OWGA were primarily employed for both TX and RX pur-
poses. Their radiation patterns shown in Fig. 6 were simu-
lated based on the parameters and dimensions of the anten-
nas using CST Studio Suite. The relatively broad radiation
pattern with sidelobes in the H-plane resulted in observable
strong crosstalk between the TX and RX antennas in some
measurtements e.g. TX-RX1 . To mitigate this interference,
MMW absorbers were positioned between them, as depicted
in Fig. 4, to eliminate LOS component. The resulting CIR
with LOS component effectively removed is presented in
Fig. 8.
LOS/crosstalk elimination
with absorber
FIGURE 8. CIR for various antenna’s configuration
Nevertheless, the broad radiation pattern of the OWGAs
contributed to MMW reflections from the ground in front of
the antennas, which, in turn, affected the RX antenna and
first received components are visible after few meters. To
address this issue, horn antenna, characterized by a narrower
half power beam width (HPBW), see Fig. 6, was also used at
position RX1, leading to signal space filtering. Fig. 8 depicts
all the mentioned antenna configurations, enabling a compar-
ison of the effects of absorbers and antenna beamwidth.
IV. CHARACTERIZATION OF PROPAGATION
PROPERTIES
An extensive measurement campaign was carried out un-
der diverse weather conditions to record detailed CIR data,
enabling comprehensive channel characterization. The mea-
surement campaign involved varying weather conditions, in-
cluding snow, ice, spring, and summer environments. The in-
vestigation into tree reflectivity involved plotting CIR data to
visualize the scattering phenomenona, as depicted in Fig. 9,
across these distinct weather conditions.
Noise floor
FIGURE 9. Comparison of CIR for identical antenna configuration ,diffuse
reflection, position of receiver correspond with fig. 2: RX1, in various weather
conditions
In this study, an OWGA was utilized as the antenna, and
a comprehensive description is provided in Section III-C.
The specific antenna configuration involved placing the TX
and RX1 antenna 5 meters apart, as depicted in Fig. 2.
The antenna’s radiation pattern led to crosstalk between the
antennas as mentioned above, along with the unintended
reflection of the signal from the ground in front of the
antennas. Additionally, the signal scattered from the tree
was detectable in the CIR after propagating a distance of
25 meters. The 80 GHz signal experienced significant path-
loss during propagation. Notably, diffuse reflections were
observed at specific distances, showcasing varying strengths
corresponding to different materials (e.g., wood, wood cov-
ered by snow/ice/foliage). The relative power exhibited vari-
ations ranging from -30 dB to -50 dB (excluding peaks). With
a tree crown diameter of approximately 3 meters, scattering
occurred at multiple points, potentially leading to signal am-
plification or attenuation due to positive or negative phase ad-
dition. In the case of FOLIAGE, where the tree was covered
with leaves, the majority of the signal was scattered, allowing
only minimal signal penetration into the inner part of the tree.
Conversely, the ICY and SNOW sub-scenarios exhibited a
more consistent reflected power curve ranging from -35 dB
to -45 dB. The weakest diffuse reflection was observed at
the start of spring when the tree lacked foliage, resulting in
relative power variations ranging from -45 dB to -55 dB.
On the other hand, when investigating the propagation
of the signal through the tree, the details of the CIR are
illustrated in Fig. 10. In accordance with the assumption
that the signal is scattered, the strongest received signal was
recorded in the ’NO-SNOW, NO-FOLIAGE’ sub-scenario,
quantified at approximately -15 dB. Compared to the specific
scenario of ’direct path 180°’, in other cases the attenuation
exceeds 5 dB, which is consistent with the observation that a
greater part of the signal was scattered in the ’SNOW’, ’ICY’,
and ’FOLIAGE’ sub-scenarios.
This measurement enables the analysis of the impact of
trees on signal attenuation. According to (1) and the defi-
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FIGURE 10. Comparison of CIR for identical antenna configuration, 180°
through tree, position of receiver correspond with fig. 2: RX2, in various
weather conditions
nition provided in [39], the free space path loss (FSPL) is
calculated as 100 dB for a distance of 29.17 meters at the
center frequency of 82.6 GHz.
FSPL = 20 log10(d) + 20 log10(f) + 20 log10 4π
c
= 100 dB
(1)
Where drepresents the distance between the TX and RX
antennas, fis the center frequency, and cis the speed of
light. At the beginning of each measurement, we perform a
system calibration, detailed in Chapter III-B, which sets the
peak amplitude of the CIR to 0 dB. In this particular case, we
used an OWGA with a gain (G) of 7 dBi at both the RX and
TX. The expected relative received power (RP)
RP =AT T +GRXAN T +GT X AN T −F SP L
= 79 + 7 + 7 −100 = −7dB,(2)
which allows the signal attenuation to be investigated. Table 3
provides the attenuation of the tree for all sub-scenarios.
TABLE 3. Tree Attenuation for Specific Weather Conditions in Signal
Propagation
Scenario- direct path 180° Attenuation [dB/m]
Snow 5.17
Icy 4.33
No-Snow 2.67
Foliage 4.83
A. PATH LOSS MODEL FOR SIGNAL PROPAGATION
THROUGH TREE
Signal power decays exponentially over distance, appearing
as a straight line when depicted on a logarithmic scale.
Fig. 11 provides a visual representation of the path loss.
The gray lines represent various measurements taken under
all weather conditions, while the solid colored lines show
the calculated mean values of thousands of snapshots taken
from many measurements, differentiated by color according
to the weather. This helps to minimize the impact of small-
scale fading. To capture the linear signal decay, trendlines are
used and depicted by dashed lines for each different weather
condition. These trendlines represent the path loss channel
model for the attenuation of the deciduous tree during various
weather conditions.
FIGURE 11. Path loss for signal propagation through tree TX - RX2
The results are consistent with the assumptions presented
in Section IV, demonstrating that signal propagation through
bare trees experiences less attenuation. Conversely, when
trees are covered by snow, ice, or leaves, the received signal
strength is notably weaker.
B. RMS DELAY SPREAD
The RMS delay spread is calculated from power delay profile
(PDP) according to [40] :
στ,n =sPi=1
LP(τi, n)τ2
i
Pi=1
LP(τi, n)−(Pi=1
LP(τi, n)τi)2
(Pi=1
LP(τi, n))2,(3)
where P(τi, n) = E{|h(τi, n)|2}are the PDP taps at the
delay τiand Lis the number of taps. Here, h(τi, n)denotes
the complex channel impulse response at the delay τifor the
n-th measurement. The noise floor was determined from the
measured data, representing the noise level before antenna
crosstalk, and it was found to be -55 dB, as illustrated in
Fig. 9.
In Fig. 12, a graph illustrating the computed RMS delay
spread for various TX vs. RX antenna configurations during
distinct weather conditions is presented. The majority of con-
figurations were measured under all four weather conditions.
However, a subset of configurations was experimentally mea-
sured only once, and this data has also been included into
the graph. Each antenna configuration is represented by a
box. The line inside the box represents the median, and the
bottom and top edges of the box represent the lower and
upper quartiles, respectively. The whiskers indicate the lower
and upper extremes, and small circles correspond to outliers.
It is evident that the RMS delay spread varies between 10 ns
VOLUME 4, 2016 7
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and 70 ns across all configurations, except for the ’through
2 trees’ propagation scenario. It corresponds to a coherence
bandwidth, BC= 1/στ,n , between 100 and 14.3 MHz. This
variation can be attributed to the unique characteristics of this
configuration, specifically the distances between TX and RX
as well as the presence of obstacles.
FIGURE 12. RMS delay spread for deciduous tree and various weather
conditions
A better conditions for signal propagation are achieved
when employing horn antennas (RX1), resulting in lower
signal dispersion, approximately 20 ns, which remains con-
sistent across various weather conditions. Similar values are
obtained for ’direct path 145°’ antennna configuration(RX3),
because of presence strong LOS components. In the case
’direct path 105°’ (RX4) the values are slightly higher, about
35 ns, due to the inherent radiation pattern of the OWGA,
detailed in Fig. 6. In the ’direct path 180°’ configuration
(RX2), where a tree was positioned between the antennas,
the RMS delay spread extends to approximately 60 ns.
For the ’through 2 trees’ configuration (RX5), the RMS
delay spread values vary from 50 ns to 180 ns, to a coherence
bandwidth between 20 and 5.5 MHz. This variability can be
attributed to differences in tree structure and ground surface.
The signal can efficiently reach the RX antenna during sub-
scenarios ’SNOW’ and ’ICY’ by reflecting off of branches
and the ground. Conversely, in sub-scenario ’FOLIAGE’, a
substantial portion of the signal is absorbed and scattered
by leaves. Only a fraction of the signal propagates through
the first tree, with the remaining power dispersing into the
surroundings, taking various paths to reach the RX antenna.
C. RICIAN K FACTOR
The Rician K-factor represent K= 10 log10 r2/2σ2,
where r2denotes the power of the LOS and 2σ2the variance
of the MPC [41].
The Kfactor quantifies the relative strength of the direct
path signal compared to the scattered signals, which allows
to estimate channel characteristics and optimize system pa-
rameters for reliable communication [42].
Low Rician K-factor indicates a channel with dominant
MPC and a weak LOS component. In such cases, the received
signal exhibits significant fading due to the interference of
multiple reflected paths. On the other hand, a high Rician
K-factor signifies a channel where the LOS component is
much stronger than the scattered components, resulting in
less severe fading [43], [44].
The observed results align with the assumption that scatter-
ing induces numerous significant MPC, while direct propaga-
tion predominantly features a LOS component. This results
in a significantly higher Rician K-factor for the direct path
(RX2), approximately 5 to 15 dB higher compared to the
diffuse reflection (RX1).
direct path 105°
direct path 145°
direct path 180°
refl + absorbers close tx
refl horn ant
refl horn+abs
reflection
reflection + absorber
reflection heavy snowfall
through 2 trees
Scenario
15
20
25
30
35
40
45
K [dB]
Rician K Factor
FOLIAGE
ICY
NO-SNOW
SNOW
FIGURE 13. Rician K-factor for deciduous tree and various weather
conditions
The highest Rician K-factor was observed in the ’direct
path 145°’ configuration (RX3). This can be attributed to
the use of an OWGA, resulting in stronger LOS components
due to the absence of obstacles in the transmission path, as
opposed to the ’direct path 180°’ configuration (RX2) where
a tree in the direct path introduced more MPCs and conse-
quently, the Rician K-factor is approximately 5dB lower.
In the case of the ’direct path 105°’ configuration (RX4), a
strong LOS component is still present, but significant MPCs
were received due to scattering from the tree and ground.
This is because the TX antenna is directed towards the tree,
and the RX antenna is positioned to receive the scattered
components.
In the ’through 2 trees’ configuration (RX5), LOS com-
ponent also dominated, but the presence of more obstacles
in the signal path led to an increase the number of MPCs.
The noticeable differences between these sub-scenarios are
attributed to the varying surface reflectivity throughout the
seasons. The tree covered with foliage predominantly scat-
tered most of the transmitted signal, resulting in the weakest
Rician K-factor for this sub-scenario.
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V. VALIDATION OF TREE FOLIAGE REFLECTION IN
SEMI-ANECHOIC CHAMBER
The measured data clearly indicate that the diffuse reflection
from a tree with foliage is higher than that from a bare tree
without leaves. To validate this finding, we performed valida-
tion measurements in a semi-anechoic chamber, as shown in
Fig. 14. Since the chamber is not suitable for MMW frequen-
cies, its opposite wall was covered using pyramidal absorbers
having low reflectivity within the desired frequency band.
The horn antennas were used for TX and RX to suppress
the spatial reflections. The blue curve in Fig. 15 provides
the CIR for the empty chamber and confirms the suitability
of the chamber for the experiment. A significant reflection is
obvious only from the opposite wall at a distance of 6 meters.
After the initial validation measurement, we acquired two
branches with leaves from the same type of tree and placed
them inside the chamber to simulate a small tree. Subse-
quently, we irradiated these branches and measured the CIR
to observe the scattering from the tree. The resulting data was
then overlaid on the same graphs, see Fig. 15. Strong peaks,
indicating diffuse reflections from branches with foliage, are
clearly visible after approximately 5 meters. Remarkably, the
scattered signal was more than 30 dB stronger than the noise
floor.
Subsequently, we plucked leaves from the branches and
conducted the measurement again. It is crucial to note that
the positions of the branches, TX, and RX antennas remained
consistent throughout all measurements. The CIR for the
branches without foliage is depicted in graphs with pink
color. The comparison highlights a significant reduction in
scattering, exceeding 15 dB, when the branches were without
leaves.
VI. CONCLUSIONS
The comprehensive analysis of MMW propagation across
various weather conditions has yielded important insights
into wireless communication at high frequencies. The results,
obtained through CIR analysis, confirm great influence of
weather conditions and foliage on signal propagation, i.e., on
reflection, scattering, and absorption. Particularly, deciduous
trees, abundant with leaves rich in water molecules, exhibited
a significant increase in scattering compared to bare trees.
A validation measurement conducted in an semi-anechoic
chamber revealed that the reflected signal from a bare tree
was attenuated by more than 15 dB compared to a tree with
foliage. Conversely, when the signal propagated through a
tree, the attenuation was almost 10 dB higher compared to a
bare tree or one covered by snow or ice. The attenuation of
a bare tree was determined at 2.67 dB/m, while the presence
of foliage increased the attenuation to 4.83 dB/m. The RMS
delay spread ranges between 10 ns and 70 ns based on anten-
nas configuration and weather conditions. And the Rician K-
factor analysis shows that in case of direct path between TX
and RX, there is less amount of MPC than in case of diffuse
reflection and the difference is about 5 to 15 dB. Investigating
signal propagation affected by trees during different seasons
a)
b)
c)
FIGURE 14. Photos from validation measurement a) scattering from tree with
or b) without foliage in semi-anechoic chamber c) empty chamber for
validation measurement
Wall of the room
Tree
FIGURE 15. Validation measurement in semi-anechoic chamber
and in different TX and RX antenna configurations presented
in this paper provides, to the best of our knowledge, new
unpublished insights.
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content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3472003
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Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
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RADEK ZAVORKA , *1996, received the Master
degree in communication and radio engineering
from Brno University of Technology, Brno, Czech
Republic, in 2020. He is currently pursuing the
PhD degree at BUT. His research interests include
millimeter wave communications, channel model-
ing, and deep learning for wireless communica-
tions.
TOMAS MIKULASEK received his master de-
gree and doctoral degree from the Brno University
of Technology in 2009 and 2013, respectively. At
the present, he is with the Department of Radio
Electronics, Brno University of Technology in
the Czech Republic, as a researcher. His research
interest is focused on design of centimeter and
millimeter wave antennas.
JOSEF VYCHODIL received the master’s degree
and doctoral degree from the Brno University of
Technology in 2013 and 2022, respectively. His
research interests include the ultra-wide band and
millimeter wave band channel measurements tech-
niques and channel emulation. His other interests
are signal processing and RFID systems.
JIRI BLUMENSTEIN received the Ph.D. degree
from the Brno University of Technology, in 2013.
In 2011, he was a Researcher with the Institute of
Telecommunications, TU Wien. He is currently a
Researcher with the Department of Radio Elec-
tronics, Brno University of Technology. His re-
search interests include signal processing, physical
layer of communication systems, channel char-
acterization and modeling, and wireless system
design.
ANIRUDDHA CHANDRA received the B.E.,
M.E., and Ph.D. degrees from Jadavpur Univer-
sity, Kolkata, India, in 2003, 2005, and 2011,
respectively. He joined the Electronics and Com-
munication Engineering Department, National In-
stitute of Technology, Durgapur, India, in 2005
and is currently an Associate Professor. In 2011,
he was a Visiting Lecturer with the Asian Institute
of Technology, Bangkok. From 2014 to 2016, he
worked as a Marie Curie Fellow with the Brno
University of Technology, Czech Republic. In 2019, he was a visiting
researcher at the Slovak University of Technology, Slovakia. In 2022, he
was a Guest Researcher at Niigata University, Japan. He has published about
130 research papers in refereed journals and peer-reviewed conferences. His
primary area of research is physical layer issues in wireless communication.
Dr. Chandra is a co-recipient of the Best Short Paper Award at IEEE VNC
2014, held in Paderborn, Germany, and delivered a keynote lecture at IEEE
MNCApps 2012, held in Bengaluru, India. Currently, he is serving as the
Secretary of the IEEE P2982 Standard Working Group, IEEE ComSoc RCC
SIG on Propagation Channels for 5G and Beyond, and vice-chair of the
subcommittee for the mid-band frequencies of the IEEE P1944 Standard
Working Group.
HUSSEIN HAMMOUD received his M.S. degree
in Communication and Radio Engineering from
the University of Southern California (USC). In
2024, he finished his Ph.D. at USC, where his
research focused on millimeter-wave communica-
tions and advanced channel modeling techniques.
His work primarily involves the design and imple-
mentation of channel sounding systems, as well as
the analysis of propagation characteristics.
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JAN M. KELNER was born in Poland, in 1977.
He received the M.Sc. degree (Hons.) in applied
physics and the Ph.D. degree in telecommunica-
tions from the Military University of Technology
(MUT), Warsaw, Poland, in 2001 and 2011, re-
spectively, and the D.Sc. (Habilitation) degree in
information and communication technology from
the AGH University of Science and Technology,
Krakow, Poland, in 2020. He is currently an Asso-
ciate Professor at the Institute of Communications
Systems, Faculty of Electronics MUT, where he started working, in 2003.
From 2021 to 2024, he was the Institute Director, and since September 2024,
he has been the Faculty Dean. From 2017 to 2024, he has been a Principal
Voting Member with the Information Systems Technology Panel operating
within the NATO Science and Technology Organization. He has been an
Expert in the European Defence Agency (EDA) CapTech Information and
the Office of Electronic Communications since 2019 and 2022, respectively.
Since 2001, he has been involved in many research and development projects
for the National Ministry of Defence, EDA, National Centre for Research
and Development, and National Science Centre. Currently, he is the Manager
of four research projects and the supervisor for ten Ph.D. students. He has
authored or co-authored more than 220 articles in peer-reviewed journals
and conferences. He is a reviewer for 35 scientific journals and about 20
conferences. His current research interests include wireless communications,
modeling and measurements of radio channels, quality of services, and
localization techniques.
CEZARY H. ZIÓłKOWSKI was born in Poland,
in 1954. He received M.Sc. and Ph.D. de-
grees from the Military University of Technology
(MUT), Warsaw, Poland, in 1978 and 1993, re-
spectively, both in telecommunications engineer-
ing. In 1989, he received a M.Sc. degree from
the University of Warsaw in mathematics (spe-
cialty – applied mathematical analysis). In 2013,
he received a D.Sc. (Habilitation) degree in Radio
Communications from MUT. From 1982 to 2013,
he was a researcher and lecturer, and he has been an Associate Professor
at the Faculty of Electronics MUT since 2013. He was engaged in many
research projects, especially in the fields of radio communications systems
engineering, radio wave propagations, radio communication network re-
sources management, and electromagnetic compatibility in radio commu-
nication systems. He is an author or co-author of more than 200 scientific
papers and research reports.
THOMAS ZEMEN received the Dipl.-Ing. de-
gree (with distinction) in electrical engineering
in 1998, the doctoral degree (with distinction) in
2004 and the Venia Docendi (Habilitation) for
"Mobile Communications" in 2013, all from Vi-
enna University of Technology. Thomas Zemen
is Principal Scientist at AIT Austrian Institute of
Technology, Vienna, Austria, leading the reliable
wireless communications group. He joined AIT in
2014 and took on the role of thematic coordinator
for physical layer security in 2018. From 2003 to 2014 he was with
FTW Forschungszentrum Telekommunikation Wien heading the Signal and
Information Processing department since 2008. From 1998 to 2003 Thomas
Zemen worked as hardware engineer and project manager for the radio
communication devices department, Siemens Austria. He has authored four
book chapters, 39 journal papers, more than 119 conference communications
and two patents. His research interest focuses on the interplay of the physical
wireless radio communication channel with other parts of a communication
system in time-critical applications.
CHRISTOPH MECKLENBRÄUKER (Senior
Member, IEEE) received the Dipl.-Ing. degree
(Hons.) in electrical engineering from Technische
Universität Wien, Vienna, Austria, in 1992, and
the Dr.-Ing. degree (Hons.) from Ruhr-Universität
Bochum, Bochum, Germany, in 1998.,From 1997
to 2000, he worked at Siemens AG Austria and en-
gaged in the standardization of UMTS. From 2000
to 2006, he held a senior researcher position with
the Telecommunications Research Center Vienna
(FTW), Vienna. In 2006, he joined TU Wien as a Full Professor. From
2009 to 2016, he has led the Christian Doppler Laboratory for Wireless
Technologies for Sustainable Mobility. He has authored approximately 250
papers in international journals and conferences, for which he has also
served as a reviewer and was granted several patents in the field of mobile
cellular networks. His current research interests include 5G and 6G radio
interfaces (vehicular connectivity and sensor networks) and antennas and
propagation. He is a member of the Antennas and Propagation Society, the
Intelligent Transportation Society, the Vehicular Technology Society, the
Signal Processing Society, VDE, and EURASIP. His doctoral dissertation
received the Gert-Massenberg Prize, in 1998. He is the Councilor of the
IEEE Student Branch Wien.
ALES PROKES received the M.Sc., Ph.D., and
the Habilitation degrees from the Brno University
of Technology (BUT), in 1988, 1999, and 2006,
respectively. Since 1990, he has been with the
Faculty of Electrical Engineering and Communi-
cation, BUT, where he is currently a Professor.
Since 2013, he has been the Head of the Research
Center of Sensor, Information and Communica-
tion Systems, Radio-Frequency Systems Group.
His research interests include measurement and
modeling of channels for V2X communication, optimization, and design
of optical receivers and transmitters for free-space optics (FSO) systems,
influence of atmospheric effects on optical signal propagation, evaluation
of FSO availability and reliability, higher order non-uniform sampling and
signal reconstruction, and software-defined radio.
12 VOLUME 4, 2016
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3472003
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
