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The paper has been presented at the 2024 IEEE Conference on Antenna Measurements and Applications (CAMA),
Da Nang, Vietnam, October 9-11, 2024
Long-Term Channel Analysis at 60 and 80 GHz
for Autonomous Ground Vehicles
Radek Zavorka∗, Tomas Mikulasek∗, Josef Vychodil ∗, Jiri Blumenstein∗, Hussein Hammoud†,
Wojtu´
n Jarosław §, Aniruddha Chandra ‡, Jan M. Kelner §, Cezary Zi´
ołkowski §, Ales Prokes ∗
∗Department of Radio Electronics, Brno University of Technology, Brno, Czech Republic
†University of Southern California, Los Angeles, USA
‡National Institute of Technology, Durgapur, India
§Institute of Communications Systems, Faculty of Electronics,
Military University of Technology, 00-908 Warsaw, Poland
e-mail: xzavor03@vutbr.cz
Abstract—This paper presents a comprehensive mea-
surement campaign aimed at evaluating indoor-to-indoor
radio channels in dynamic scenarios, with a particular
focus on applications such as autonomous ground vehicles
(AGV). These scenarios are characterized by the height
of the antennas, addressing the unique challenges of
near-ground communication. Our study involves long-term
measurements (20 minutes of continuous recording per
measurement) of the channel impulse response (CIR) in the
60 GHz and 80 GHz frequency bands, each with a band-
width of 2.048 GHz. We investigate the variations in chan-
nel characteristics, focusing on parameters such as root
mean square (RMS) delay spread and the Rician K-factor.
Index Terms—long-term measurement, channel mea-
surement, millimeter waves, RMS delay spread, statistics
I. INTRODUCTION
In wireless communication, understanding how ra-
dio channels behave in dynamic environments is key
to ensuring reliable performance. This paper focuses
on a comprehensive measurement campaign aimed at
evaluating indoor-to-indoor radio channels in dynamic
scenarios. Specifically, our study is designed with em-
phasis on applications such as autonomous ground vehi-
cles (AGV), where changing conditions can significantly
affect communication quality.
Two promising frequency bands for high-speed data
transmission were analyzed, 60 Ghz and 80 Ghz, re-
spectively. The 60 Ghz band, which offers several GHz
of bandwidth, has been assigned by the International
Telecommunication Union (ITU) to the Industrial, Sci-
entific, and Medical (ISM) bands, enabling license-
free operations [1]. Additionally, the E-band covers
the 71-76 Ghz and 81-86 Ghz bands, offering 5 Ghz of
bandwidth, and has been considered as a candidate for
5G at world radiocommunication conference (WRC)-15
[2]. Compared to the 60 Ghz band, E-band waves are
less prone to oxygen absorption during atmospheric
propagation. [3].
Dynamic scenarios introduce unique challenges to
wireless communication systems. In indoor environ-
ments, movement of people, furniture, and other objects
can alter signal propagation paths, leading to variability
in signal strength and multipath effects [4]. These fluc-
tuations can impact communication reliability, latency,
and throughput, which are critical for applications that
rely on stable wireless connections [5].
Our measurement campaign involved capturing exten-
sive data across a variety of indoor settings, focusing on
how the radio channel changes over time. By analyzing
this data, we aim to identify statistical patterns and
evaluate their impact on communication performance.
The analysis includes examining metrics such as root
mean square (RMS) delay spread and Rician K-factor,
each of which offers insights into the nature of the radio
channel and its potential influence on communication
systems [6].
Maintaining regular communication is essential for
the effectiveness and safety of AGV operating indoors
[7]. In applications like warehouse automation and smart
transportation, AGV play a pivotal role [7], [8]. For
navigation, decision-making, and interacting with other
systems, they depend on reliable wireless connection.
However, operating in indoor environments introduces
challenges due to changing layouts, moving obstacles,
and varied signal paths, which can cause significant
fluctuations in strength of radio signals. The height of
communication antennae, a challenge known as near-
ground propagation [9]–[12], is specified for AGV.
A. Contribution of the Paper
In this paper, we present our measurements of the
time-variant channel in the frequency band of 60 GHz
and 80 GHz with a bandwidth of 2.048 GHz. Our objec-
tive was to investigate the variations in channel charac-
teristics and their parameters such as RMS delay spread
or Rician K-factor.
The main contributions of this paper are as follows:
•We present a long-term measurement campaign
(20 minutes of continuous recording per measure-
ment) of a dynamic channel in the frequency band
of 60 GHz and 80 GHz in an indoor-to-indoor sce-
nario with antennas position to simulate differences
between AGV and pedestrian.
This research was funded in part by the National Science Center (NCN), Poland, grant no. 2021/43/I/ST7/03294
(MubaMilWave). For this purpose of Open Access, the author has applied a CC-BY public copyright license to
any Author Accepted Manuscript (AAM) version arising from this submission.
arXiv:2503.18824v1 [eess.SP] 24 Mar 2025
•We analyze the impact of a mooving people and
objects in the vicinity of transmitter (TX) and
receiver (RX) antennas and its influence on radio
channel properties.
B. Paper organization
The organization of the remaining sections is as
follows: Section II provides a detailed description of
the measurement campaign. Section III discusses the
measurement setup, including the equipment and con-
figurations used, with Subsection III-A explaining the
principle of long-term data recording. In Section IV, we
provide a statistical analysis of the indoor radio channel,
focusing on the RMS delay spread and Rician K-factor
in Subsections IV-A and IV-B, respectively. Finally, in
Section V, we summarize the key findings and present
the conclusions of this work.
II. MEASUREMENT CAMPAIGN DESCRIPTION
The measurement campaign has been focused on the
statistical channel description in a dynamic environment
and has been performed at Faculty of Electrical Engi-
neering and Communication (FEEC), Brno University of
Technology (BUT), Technicka 12, Brno, Czech Repub-
lic. TX and RX were in static position and environment
was changing in time- pedestrians were present, the glass
door was being opened and closed, causing multipath
components (MPC). The TX position was next to the
main entrance to the building and RX was placed in
the faculty library with antennna orientation to the TX.
An open-ended waveguide antenna was used at the TX.
This setup demonstrated a base station with a wide beam
antenna designed to cover a broader area. At the receiver,
a horn antenna was employed to improve signal strength
and to mitigate MPC. Locations of TX and RX in the
building are shown in a floor plan in Fig. 1 with line-
of-sight (LOS) highlighted.
Fig. 1. Plan of the measured scenario - floor plan
The measurement campaign was focused on two fre-
quency bands, 60 GHz and 80 GHz, respectively. As was
mentioned in Chapter I, AGV could be used to distribute
books from library or other equipment across institute
in building. To ensure reliable communication and safe
operation, it is necessary to understand the behavior of
the millimeter wave (MMW) propagation. TX antenna
was positioned in 1.4 m and height of RX antenna was
variable in two position - 1.4 m and 0.8 m, respectively.
The goal was to compare the standard operating height
for a mobile phone in the hand or in a breast pocket and
the height of the antenna on an AGV for near ground
communication. We captured a twenty-minute recording
of each scenario to enable statistical evaluation of long-
term channel measurements. One scenario at 60 GHz,
the RX antenna in a low position, was measured twice
at different times to eliminate randomness and allow
comparison of an identical scenario.
III. MEASUREMENT SETUP
The schematic representation of the measurement
setup is shown in Fig. 2. The board ZCU111 of Xilinx
Zynq UltraScale+ RFSoC is used as a transmit baseband
subsystem. The I and Q components of an intermediate
frequency signal are produced by fast DACs clocked
at 6.144 GSPS. The excitation signal was a frequency
modulated continuous wave (FMCW) with ramp-up and
ramp-down to provide the flattest feasible spectrum.
This decision was primarily motivated by the fact that
the FMCW signal exhibits remarkable robustness in the
presence of system non-linearities. With a bandwidth
B= 2.048 GHz and duration T= 8 µs , the FMCW al-
lows for fast measurements up to fmeas =1
T= 125 kHz
(measurements per second) while keeping an acceptable
signal to noise ratio (SNR). Averaging can be used to
further increase the SNR in static scenario.
The Sivers IMA FC1005V/00 (60 GHz) or
FC1003E/03 (80 GHz) up/down converter raises
the signal to the required millimeter wave frequency.
For the upconversion, a frequency stable, low phase
noise local oscillator signal is supplied by the Agilent
83752A generator. The QuinStar QPW-50662330-C1
(60 GHz) or Filtronic Cerus 4 AA015 (80 GHz) power
amplifier boosts the RF signal power, which is then
transmitted using open-ended waveguide antenna.
The radiation patterns for the 60 GHz open waveguide
antenna (OWGA) and horn antenna are shown in Fig. 3.
Fig. 4 shows the radiation patterns for the 80 GHz
OWGA and horn antenna. More information for both
bands is provided in Tab. I and Tab. II.
TABLE I
PARAMETERS OF OWGA A ND H OR N ANT EN NA AT 60 GH Z
OWGA Horn
Gain [dBi] 7 20
HPBW E-plane [°] 88 14
TABLE II
PARAMETERS OF OWGA A ND H OR N ANT EN NA AT 82 GH Z
OWGA Horn
Gain [dBi] 6.4 20
HPBW E-plane [°] 52 16
A processing chain that is similar to the transmitting
side of the measuring setup receives the signal after it has
traveled through the measured environment. The Quin-
star QLW-50754530-I2 (60 GHz) or Low noise factory
LNF-LNR55 96WA SV (80 GHz) low noise amplifier
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
Fig. 2. Measurement system schematic
-150 -100 -50 0 50 100 150
Theta [deg]
-40
-30
-20
-10
0
10
20
Gain [dBi]
14
88
Open-ended: E-plane
Open-ended: H-plane
Horn: E-plane
Horn: H-plane
Fig. 3. Simulated radiation pattern of OWGA and horn antenna at
60 GHz
-150 -100 -50 0 50 100 150
Theta [deg]
-50
-40
-30
-20
-10
0
10
20
Gain [dBi]
16
52
Open-ended: E-plane
Open-ended: H-plane
Horn: E-plane
Horn: H-plane
Fig. 4. Simulated radiation pattern of OWGA and horn antenna at
82 GHz
amplifies the signal after it is received by the horn
antenna. Using a local oscillator signal produced by the
Agilent E8257D generator, the Sivers IMA FC1003V/01
(60 GHz) or FC1003E/02 (80 GHz) up/down converter
performs the downconversion. After being sampled at
an intermediate frequency in the form of its I and
Q components, the signal is recorded to an SSD for
additional processing by using the fast ADCs (clocked at
4.096 GSPS) of another Xilinx Zynq UltraScale+ RFSoC
ZCU111 board. More information about the testbed and
calibration process can be found in [13].
A. Principle of long-term data recording
The measurement setup, based on the Xilinx Zynq
UltraScale+ RFSoC ZCU111 board as mentioned above,
enables long-term data recording. In each clock cycle,
256 bits (32 bytes) of data are stored, with each sample
comprising 4 bytes (16 bits I, 16 bits Q). This allows
for the storage of 8 samples per cycle. Given the large
volume of data generated, continuous recording for tens
of minutes is not feasible. The method for long-term
data recording is illustrated in Fig. 5. The window
length is set to 8,388,608 samples, corresponding to
4096 µs. From each window, the first 16,384 samples,
equivalent to 8 µs, are retained, while the remaining data
is discarded. To store 20 minutes of data, M=300,000
windows are recorded, corresponding to 1,228.8 s and
requiring 18.31 GB of storage.
window length
capture
length
M = 1 M = 2 M = 300,000
4096 us
8 us
20.48 minutes, 18.31 GB
16,384
samples
8,388,608 samples
Fig. 5. Graphic representation of long-term data recording
IV. STATISTICS OF INDOOR CHANNEL
This section presents a statistical analysis of long-term
measured data in the 60 GHz and 80 GHz frequency
bands, along with the extraction of parameters describing
the channel.
We used our time domain channel sounder to estimate
the channel impulse response (CIR) [14]
hm(t, τ ) =
N(t)
X
n=1
αn(t)ej2πfDtδ(τ−τn(t)),(1)
where mis a measurement index and Nis the number
of propagation paths. The variables αn(t)and τn(t)
corresponds to the amplitude and delay of the n-th
propagation path while δis the Dirac impulse and fDis
the Doppler frequency.
Temporal variations in the CIR are illustrated in Fig. 6.
Observable deviations are attributed to environmental
factors such as human movement and the opening or
closing of doors, which occasionally obstructed the
direct signal path. In addition to capturing the CIR as
I and Q samples, video footage was recorded from the
perspective of the RX antenna to facilitate the analysis
of the specific causes of MPC deviations. A video frame
corresponding to the specific MPC of CIR highlighted
in Fig. 6 is presented in Fig. 7.
Fig. 6. CIR of long-term measurement at 80GHz, RX antenna at high
position
A. RMS delay spread
The RMS delay spread is calculated from power delay
profile (PDP) according to [15] :
Fig. 7. Video frame from the measurement campaign corresponding
to the highlighted MPC in the CIR shown in Fig. 6
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,
(2)
where P(τi, n) = E{|h(τi, n)|2}are the PDP taps at the
delay τi,Lis the number of taps and h(τi, n)denotes
the complex channel impulse response at the delay τi
for the n-th measurement.
We recorded 20 minutes of data for each scenario,
allowing us to compute how the RMS delay spread
changes over time. The cumulative distribution function
(CDF) of the RMS delay spread is depicted in Fig. 8.
It is visible that the shape of the CDF is consistent
across the frequency bands (blue, red vs. yellow curve
(60 GHz) and purple vs. green (80 GHz) ); however,
there is a shift along the x-axis depending on the height
of the RX antenna. In one case, we performed the
measurement twice for the same configuration (60 GHz
and antenna at a low position) to eliminate randomness.
It was confirmed that the CDF for both measurements
is almost the same.
0.5 1 1.5 2 2.5
RMS delay spread [s] 10 -8
0.2
0.4
0.6
0.8
1
Probability
RX60low1
RX60low2
RX60high
RX80low
RX80high
Fig. 8. Cumulative distribution function of RMS Delay Spread
The RMS delay spread is higher at 80 GHz due to
the shorter wavelength, which increases the likelihood of
reflections from smaller objects. Additionally, significant
differences are observed based on antenna height. Anten-
nas positioned at higher elevations capture more MPC,
leading to an increased RMS delay spread compared to
antennas at lower positions. This trend is evident in both
frequency bands.
B. Rician K-factor
The Rician K-factor is given by
K= 10 log10 r2
2σ2,(3)
where r2represents the power of the LOS component
and 2σ2denotes the variance of the MPC [16].
The CDF of the Rician K-factor is depicted in Fig. 9.
The distribution of the graph curves corresponds to the
assumption according to the results from RMS delay
spread.
6 8 10 12 14 16 18 20 22
Rician K Factor [dB]
0
0.5
1
Probability
Cumulative distribution function (CDF) of Rician K Factor
RX60low1
RX60low2
RX60high
RX80low
RX80high
Fig. 9. Cumulative distribution function of Rician K-factor
The highest K-factor is observed at 60 GHz with
the antenna positioned at a higher elevation. This is
attributed to the longer wavelength at this lower fre-
quency, which results in fewer MPC and a stronger
line-of-sight (LOS) component relative to the MPC. The
K-factor decreases by approximately 3 dB when the
antenna is positioned lower. In the E-band, the increased
reflections from various objects result in a lower K-
factor. Similarly, antenna height plays a significant role:
a higher antenna position corresponds to a higher K-
factor, and a lower antenna position corresponds to a
lower K-factor, respectively.
V. CONCLUSION
The primary contributions of this paper include an
analysis of a dynamic channel in indoor scenarios, where
the positioning of the antennas is used to simulate
differences between AGV and pedestrian communication
environments. Additionally, we examine the impact of
moving people and objects in the vicinity of the TX
and RX antennas, and their influence on radio chan-
nel properties. Our analysis primarily focuses on the
evaluation of the RMS delay spread and the Rician K-
factor, revealing a significant influence of antenna height
and radio signal frequency. The RMS delay spread is
higher in the E-band by approximately 5–10 ns, and it
also increases for higher antenna positions at the same
frequency band. In line with these findings, the K-factor
is higher at 60 GHz and decreases by approximately 3 dB
when the antenna is positioned lower. These insights into
the behavior of high-frequency dynamic channels in en-
vironments relevant to AGV operations contribute to the
development of more robust and reliable communication
systems for such applications.
ACKNOWLEDGMENT
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. Kel-
ner, Jarosław Wojtu´
n and Cezary H. Zi´
ołkowski 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’.
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