# Tuning of Ray-Based Channel Model for 5G Indoor Industrial Scenarios

Gurjot Singh Bhatia, Yoann Corre  Gurjot Singh Bhatia12, Yoann Corre1, M. Di
Renzo2  1 SIRADEL, Saint-Gregoire, France
2 Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et
Systèmes, Gif-sur-Yvette, France
<EMAIL_ADDRESS>
###### Abstract

This paper presents an innovative method that can be used to produce
deterministic channel models for 5G industrial internet-of-things (IIoT)
scenarios. Ray-tracing (RT) channel emulation can capture many of the specific
properties of a propagation scenario, which is incredibly beneficial when
facing various industrial environments and deployment setups. But the
environment’s complexity, composed of many metallic objects of different sizes
and shapes, pushes the RT tool to its limits. In particular, the scattering or
diffusion phenomena can bring significant components. Thus, in this article,
the Volcano RT channel simulation is tuned and benchmarked against field
measurements found in the literature at two frequencies relevant to 5G
industrial networks: 3.7 GHz (mid-band) and 28 GHz (millimeter-wave (mmWave)
band), to produce calibrated ray-based channel model. Both specular and
diffuse scattering contributions are calculated. Finally, the tuned RT data is
compared to measured large-scale parameters, such as the power delay profile
(PDP), the cumulative distribution function (CDF) of delay spreads (DSs), both
in line-of-sight (LoS) and non-LoS (NLoS) situations and relevant IIoT channel
properties are further explored.

###### Index Terms:

channel models, 5G, benchmark, ray-tracing, mmWave.

##  I Introduction

The 5G mobile communication was developed to enhance mobile networks’
broadband capabilities and supply improved wireless access to a wide range of
industry verticals, such as the manufacturing, automotive, and agricultural
sectors [1]. Industrial environments are considered severe from the point of
view of electromagnetic (EM) wave propagation. A large number of obstructions
and fading fluctuations may cause degraded signal and system reliability.
Hence, radio channel characterization is critical for designing radio
communication systems for future smart factories.

A channel model is known as an abstract and simplified approach to
mathematically or computationally reproduce the main characteristics of an
actual channel and evaluate the impact on the performance of a specific
wireless technology.

Empirical channel models rely on wide-band channel measurements to
characterize propagation by statistically assessing wide-band channel
properties and then formulating mathematical relationships and equations to
derive important variables like path loss, DS, angular spread, and so forth.
Some popular empirical path loss models for indoor scenarios are: Log-Normal
(Large-scale) Shadowing model, Alpha Beta Gamma (ABG) model, and Close-in (CI)
free space path loss model. The 3rd Generation Partnership Project (3GPP) in
the technical report (TR) 38.901 version 16.1.0 [2] also supports the ABG
model and CI free space path loss model for indoor scenarios, such as Indoor
Hotspot - Office (InH) and Indoor Factory (InF) scenarios.

Non-geometric Stochastic Channel Models (NGSCMs) illustrate and determine
physical parameters entirely stochastically by dictating underlying
probability distribution functions without assuming an underlying geometry
(examples are the Saleh-Valenzuela or the Zwick model). Whereas, Geometry-
based Stochastic Channel Models (GSCMs) rely on some geometrical assumptions,
and the propagation parameters are at least partially stochastic and specified
by probability distributions [3]. The COST models, the 3GPP Spatial Channel
Model (SCM), and Wireless World Initiative New Radio (WINNER) are some
examples of GSCMs.

Stochastic channel models (SCMs) do not aim to reproduce channel responses at
a particular site but are plausible and realistic given an imaginary
environment. SCMs leverage radio measurements such as wide-band channel
measurements to derive statistical distributions for large-scale parameters
and distribution laws for multi-path components (MPCs).

Deterministic channel modeling is another approach to simulate radio
propagation based on Maxwell’s equations. RT is a commonly-used technique in
deterministic channel modeling. Its algorithm starts by finding all the
possible geometrical rays between a transmitter and a receiver for a given
number of allowed interactions. Then, the calculation of the rays’ (EM field)
contributions is based on the geometrical optics (GO), uniform theory of
diffraction for diffraction (UTD), and effective roughness theory (ER),
assuming that the far-field conditions are met. The RT can provide path loss
data, angle-of-arrival (AoA), angle-of-departure (AoD), time delay, optical
visibility (LoS or NLoS), etc.

RT, similar to the finite element method, can require a lot of computational
resources to produce precise results, especially for complex problems. This is
particularly consequential in industrial environments with many complicated
structures and objects with complex geometries. As a result, RT simulations
can become computationally intensive for InF scenarios.

Recently, 3GPP in the TR 38.901 version 16.1.0 [2], has proposed models for
typical smart factory environments called InF and IIoT channel models. These
channel models rely significantly on measurement data collected in typical
propagation conditions. Several measurement campaigns in InF scenarios can be
found in the literature. In [4], the authors try to assess the channel
propagation at 28 and 60 GHz frequencies for light and heavy industrial
layouts. In [5], the authors demonstrate massive multi-input multi-output
(mMIMO) channel characteristics based on channel sounding measurements carried
out in an industrial environment at frequency ranges from 26 to 30 GHz. In
[6], the authors perform directional wide-band channel measurements at 28 GHz
in an industrial environment. The work in [7] explains a wide-band channel
measurement campaign at 3.7 and 28 GHz with direction-of-arrival information
at 28 GHz. Such campaigns typically use empirical models and analyze channel
parameters such as path loss, PDP, AoA, AoD, LoS probability, and Root Mean
Square (RMS) DS, among other relevant parameters.

Stochastic and empirical channel models rely heavily on wide-band field
measurements [8, 6, 7]. Field measurements, such as channel-sounding
measurements, are used to get precise data about the radio channel. They pose
several difficulties in complexity, cost, and representability, which need
efficient radio propagation models to be developed as workable replacements.
Another challenge for those models is that propagation in InF scenarios is
more site-specific than in usual residential or office environments [4, 7].
For instance, in InF scenarios, metallic machines are one of the most common
objects in the environment. The huge bodies of such machines can become major
blockers in the NLoS case. The smooth metallic surface creates many
reflections, and the large body prevents the signal from propagating directly.
The InF environment with a large number of machines complicates the radio
propagation in the factory.

Given the drawback of field measurements, the site-specific nature of InF
scenarios, and the recent interest in mmWave frequency bands for wireless
networks, many channel and network performance metrics for mmWave
communications have been lately generated through RT [9, 10]. Hence, ray-based
channel models can be considered an interesting radio propagation and
characterization approach in InF scenarios.

In this paper, we present an approach that can be used to produce a site-
specific calibrated ray-based channel model for a typical 5G InF scenario.
This paper aims to reduce the dependency on complicated field measurements.
This will allow a tool to create site-specific channel data in factory
scenarios and a database of channel samples that may be used for research
studies. There have been earlier attempts to calibrate ray-based models for
urban scenarios using measurement data like in [10]. Still, to the best of our
knowledge, there has not been any such attempt for industrial environments
yet.

This paper is structured as follows. Section II briefly describes the various
benefits of RT for InF scenarios. Section III describes the approach used for
the validation of RT against measurements. Section IV portrays some ideas for
exploiting the calibrated RT tool. Finally, Section V presents the conclusion
and future perspective of this work.

##  II Benefits of Ray-tracing for InF scenarios

The RT approach presents some benefits to realize the next-generation channel
models for future smart factories. These benefits can provide a new paradigm
to study the signal propagation characteristics and model the wireless channel
in industrial scenarios.

  * •

RT is a powerful technique for predicting “specular MPCs” (SMPCs: specular
reflections and diffractions) and “dense MPCs” (DMPCs: diffuse scattering) in
complex industrial settings. RT enables deterministic modeling of ray paths,
making it valuable for channel modeling, network planning, and optimization,
particularly at mmWave frequencies where there are a limited number of such
paths.

  * •

RT requires detailed modeling of the propagation environment, incorporating
precise dimensions and dielectric properties of objects. Hence, RT models can
be tailored to specific scenarios, effectively capturing the site-specific
characteristics of indoor industrial environments.

  * •

The RT model can be calibrated [10]. Once a RT model is calibrated and
validated for a specific scenario, it can predict channel parameters for
different positions of the base station (BS) and the user terminal (UT), and
predict coverage maps and correlation properties. It can also predict MIMO
channel properties, spectral efficiency, and data rates.

  * •

The validated RT model then can provide received power and other large-scale
parameters at arbitrary locations for radio coverage planning and network
optimization of a communication system.

  * •

Spatial and time variability of the channel in terms of the movement of the
transmitter, receiver, and other objects (such as mobile robots, automated
guided vehicles (AGVs), drones, and humans.) in the environment can also be
included.

  * •

It is relatively easy to include beyond 5G enabling technologies such as
reconfigurable intelligent surfaces (RIS) into RT models. For instance, the
effective roughness theory used to model diffuse scattering interactions can
be used to model the anomalous reflections from RIS [11].

##  III Ray-tracing validated against measurements

This section will focus on the benchmarking attempt against the measurement
results found in the literature. In [7], the authors present a wide-band
channel measurement campaign at 3.7 and 28 GHz with direction-of-arrival
information at 28 GHz. Large-scale channel parameters are evaluated using
empirical channel models, and the results are compared to the 3GPP TR 38.901
InF model. It is an example of an industrial measurement campaign and a
typical use case for InF scenarios. Hence, it will be a reference to benchmark
and tune our ray-based model.

The 3D digital model of the measurement scenario, as shown in Fig. 1, was
created just from the 2D floor plan available in the literature. We did not
have access to a detailed description, photos of the measurement site, or
exact specifications of the measurement setup. Thus, the chosen objects, their
material, and their dimensions are a guess based on the reference paper and
analysis of typical objects found in such factories. The exact location of
different receiver positions was also unknown.

Figure 1: 3D digital model of the measurement site in channel emulator.

###  III-A The 3D digital scenario and simulation settings

The BS antenna used in simulations is a single element, $\frac{\lambda}{2}$
vertically polarized omni-directional antenna. The transmitted power was kept
at 0 dBm to analyze only the propagation channel’s impact. The UT antenna is
also a single element, $\frac{\lambda}{2}$ vertically polarized omni-
directional antenna. The BS is deployed at a fixed position at a height of
1.85 m. The UTs are deployed at 75 different positions: 1-38 LoS (step size =
1 m) and 39-75 NLoS (step size = 1 m), as shown in Fig. 1. All the UTs are at
a height of 1.44 m. The digital model is 74.4 m long, 24.4 m wide, and 4.6 m
high. Table I gives the specifications of the 3D digital scenario, while table
II gives an overview of the various objects in the 3D digital scenario.

TABLE I: Specifications of the 3D digital scenario. Parameters |  Values
---|---
Hall size ($m$) |  $74.4\times 24.4$
Hall height ($m$) |  $4.6$
Clutter density |  Low clutter density $\approx$ $18.33\%$
Average clutter height ($m$) |  2
Carrier frequency: bandwidth |  $3.7$ GHz: $80$ MHz, $28$ GHz: $100$ MHz
TABLE II: Various objects in the 3D digital scenario. Object |  Dimension ($m$)
---|---
Machines |  $6.7\times 2.4\times 2$
Storage racks |  $2.3\times 1.1\times 4$
Cupboards |  $1.8\times 1\times 3.6$
Industrial worktables |  $6\times 2\times 0.8$
Tables and Chairs |  $3.2\times 1.6\times 0.5$
Crates |  $2\times 1.5\times 1$

After creating a digital model of the measurement site, point-to-multi-points
(P2MP) simulations were realized for positions 1-38 for the LoS case and 39-75
for the NLoS case, using Volcano Flex [12] RT. Volcano Flex is a time-
efficient propagation model based on the ray-launching (also known as the
shooting-and-bouncing) approach, capable of predicting deterministic path-loss
in any small-scale urban or indoor scenario. It can provide channel properties
and 3D multi-path trajectories. By default, two reflections and one
diffraction are allowed for each ray.

For P2MP simulation results, various channel parameters, such as received
power, PDP, channel impulse response (CIR), transfer function (TF), Azimuth
AoA, Elevation AoD, and many more, can be calculated. After the initial
analysis, the simulation results of position 1 (LoS) and 39 (NLoS) were
compared with the measured results. The multi-paths richness was observed to
be strongly underestimated, and low-power components at higher delays were
missing. Hence, the number of allowed interactions was changed. The P2MP
analysis was repeated with a maximum of three reflections, one diffraction
allowed for each ray, and diffuse scattering from walls and machines were
activated [10].

The PDPs showed significant improvement for most of the positions when the
number of interactions was increased, but this increased the computation times
as well. A point-to-point (P2P) link simulation with three reflections, one
diffraction, and diffuse scattering from walls and machines took around two
and a half hours compared to about thirty minutes if diffuse scattering was
disabled. This time further decreases to a few seconds for the case of two
reflections and one diffraction for each ray, but the results worsen
significantly. Hence, the results could further be improved by increasing the
number of interactions, but this would significantly increase the computation
times.

###  III-B Calibration of channel simulation results

The calibration process aims to adjust the contribution of different types of
ray interactions and minimize the difference between the simulated and
measured results.

As mentioned earlier, metallic machines are a common object in InF situations.
Their enormous size makes them a major NLoS obstacle and scatterer for low-
elevation user devices. Machines are not represented with all details but by a
large simple block. They are associated with the material properties of a
typical metal [13].

After visual inspection of predicted rays and initial channel parameters, it
could be seen that the SMPCs (specular reflections and diffractions) from the
machines were over-estimated. Hence, to decrease the impact of SMPCs just from
the machines, the default metal properties were swapped to
$\varepsilon_{\mathrm{r}}^{{}^{\prime}}=3$,
$\varepsilon_{\mathrm{r}}^{{}^{\prime\prime}}=0.1$ at 3.7 GHz and
$\varepsilon_{\mathrm{r}}^{{}^{\prime}}=3$,
$\varepsilon_{\mathrm{r}}^{{}^{\prime\prime}}=0.09$ at 28 GHz, with a
thickness of 40 cm. This would decrease the weight of the specular reflections
and diffractions, and allow for some (minor) transmission, while the strength
of the diffuse scattering remains unchanged. These equivalent material
properties offer significant improvement. However, we did not exhaustively
analyze, and further optimization remains.

Then we used the same principle as in [10] with ray-path classification and
calibration to better fit the measurements. PDPs (maximum power and power
decay trend), DSs, and angular distributions are analyzed for this study.

Figure 2: Some of the predicted specular ray components for UT 1 (LoS) after
initial calibration.

Furthermore, the diffraction components were decreased by a diffraction offset
of 10 dB. The contribution of the DMPCs (diffuse scattering) from the channel
simulations was weaker than the measured results. Hence, the DMPCs were
increased by a diffuse scattering offset of 12 dB. This coarse correction has
no apparent physical justification but shows promising results and will permit
the realization of realistic studies.

###  III-C Comparison of calibrated and measured results

####  III-C1 Power Delay Profile

Fig. 3 (top) [7] shows the instantaneous PDP (IPDP) and averaged PDP (APDP)
from the measurement data at 3.7 and 28 GHz, respectively, for the LoS case.
The LoS PDP corresponds to a measurement point with a BS-UT distance of 5.2 m,
resulting in a time of flight (ToF) delay of 17.5 ns. At 3.7 GHz, the measured
LoS component was received with a power of -58.8 dBm, and at 28 GHz, with a
power of -78.1 dBm. Fig. 3 (bottom) also shows the simulated LoS PDP from the
RT model. It corresponds to a BS-UT distance of 6.1 m, resulting in a ToF
delay of 20.3 ns. This component was received with a power of -59.6 dBm, and
at 28 GHz, with a power of -77.1 dBm. In LoS case for both measured and
simulated results, several SMPCs can be seen together with DMPCs for both
frequencies. For measurement results, they reach the noise floor of -145 dBm
at a delay of about 450 ns at 28 GHz and 550 ns at 3.7 GHz.

Thanks to calibration, the average trend of the simulated PDP is similar to
the measurement until 180 ns. Beyond this value, the simulated power decrease
accelerates; barely any propagation path exceeds the noise floor at delays
greater than 320 ns and 400 ns at 28 GHz and 3.7 GHz, respectively.

Figure 3: Comparison between the measured PDP (top picture, taken from [7])
and the simulated PDP (bottom), for LoS case.

Fig. 4 (top) [7] shows the IPDP and APDP from the measurement data at 3.7 and
28 GHz, respectively, for NLoS case. The NLoS PDP corresponds to a measurement
point with a BS-UT distance of 9.9 m, resulting in a time of flight delay of
33 ns. Fig. 4 (bottom) also shows the simulated LoS PDP. It corresponds to a
BS-UT distance of 16.5 m, resulting in a ToF delay of 54.9 ns. For measurement
results, only a few strong SMPCs can be seen at 3.7 GHz with a delay of 40 ns
and a power of -79.4 dBm. It is difficult to distinguish between specular and
diffuse components, even in a short delay range. Besides, in the simulation,
we observe some dominant specular ray-paths at 65 and 80 ns with a power close
to -80 dBm. The maximum predicted power is consistent with the measurement.
However, the density of components detected at this level is underestimated.
The reason may come from the simplification of the machinery representation.

Figure 4: Comparison between the measured PDP (top picture, taken from [7])
and the simulated PDP (bottom), for NLoS case.

As shown in Fig. 3 and 4, the LoS and NLoS PDPs from measurement and simulated
results follow the same trend with some disagreements that can also be
attributed to the lack of detailed information about the measurement setup and
scenario. Ray-based channel models, especially for the industrial environment,
are highly scenario specific. Hence, the lack of proper details about various
industrial objects’ size, shape, position, and other aspects can change RT
results, especially for NLoS cases and mmWave propagation, where DMPCs play a
significant role.

####  III-C2 RMS DS and Angular Spread

As shown in Fig. 5, the CDF of DS was calculated using the calibrated RT
results and compared with the measurement results at 3.7 GHz. For LoS case,
the DS for the simulated scenario is smaller than the measured DS, due to
underestimated power at higher delays. For the NLoS case, the observed
difference is the opposite; this can be partly attributed to the fact the BS-
UT distances in the digital scenario are almost 1.5 times larger than in the
actual measurement scenario. The comparison also reinstates that it is hard to
distinguish between specular and diffuse components, even in a short delay
range. This complicates choosing the optimal offset for different interactions
and can undermine the multi-path richness of the channel. The horizontal
(azimuth) AoA power spectrum was also calculated and compared to the
measurements, as shown in Fig. 6.

Figure 5: Comparison between the measured CDF of RMS DS (top picture, taken
from [7]) and the simulated CDF of RMS DS (bottom), for LoS and NLoS case at
3.7 GHz. Figure 6: Comparison between the measured (top picture, taken from
[7]) and the simulated horizontal AoA power profile at 28 GHz.

##  IV Exploitation

After its calibration, the RT tool can be exploited for various applications.
We give some examples here.

Fig. 7 shows the coverage map of the factory floor at 3.7 GHz with a
resolution of 2 m. The coverage analysis can be performed with a finer
resolution, but this would cost higher computation times. Such a map is
interesting to complement the channel analysis; it provides a more extensive
and comprehensible evaluation of the factory’s power distribution beyond the
scope of what can be observed solely through measurements. For instance, we
can see different NLoS situations with higher or lower power degradation.

Figure 7: Coverage map of the factory floor at 3.7 GHz.

The calibrated model can also easily analyze channel properties between new
BS-UT positions, as shown in Fig. 8. This makes the ray-based model a flexible
tool to complement the measurement-based channel characterization.

Figure 8: Simulation results corresponding to the new BS 2 and UT 82 at 3.7
GHz.

Finally, Fig. 9 (top) shows a factory scenario that the authors in [14] use to
investigate various channel emulation use cases, like simplifying the
propagation model to improve the time efficiency. Based on a real factory
setup, three distinct zones are identified. These zones differ from each other
in terms of shape, size, object material, and clutter density. Zone A is the
biggest. It is 79.5 m long and is characterized by machines and storage
containers. Zone B is 78.6 m long and is empty primarily to facilitate the
movement of factory workers and their loads, with two big lobbies connecting a
few rooms. Zone C is 51.6 m long, with metallic lockers, wooden benches, and
metallic housing units.

Figure 9: A generic industrial scenario with CDFs of DS at 3.7 GHz of the
three different zones for NLoS case.

This scenario requires a separate in-depth study and analysis, but the first
simulations conducted with the approach mentioned in this paper are reported
here. Fig. 9 (bottom) shows that the CDFs of DS vary in a significant manner
from one zone to another. These results re-emphasize that the IIoT channel
models are site-specific, and any change in the factory scenario can strongly
impact the channel properties. Such a problem involving producing channel
samples for InF scenarios can be easier to address using a RT tool. The
approach suggested in this paper can be used to reduce the dependency on
complicated channel measurements and provide more flexibility to produce
channel models for InF and IIoT scenarios.

##  V Conclusions and future perspective

This work tested and calibrated a ray-based channel model in an industrial
scenario. The calibration steps helped improve the average trend of simulated
PDPs, DSs, and angular distribution. Several aspects need further analysis,
such as optimized material properties and extent of simplification of
representation of various objects in the environment, more ways to increase
the multi-path richness of the channel, and optimized diffraction and diffuse
scattering offset. This work is undoubtedly one of the first studies directed
toward producing a site-specific calibrated ray-based channel model for
typical 5G InF scenarios.

It is worth noting that the calibrated ray-based channel model can be used to
predict several other channel and network parameters for the given factory
scenario, such as coverage maps, correlation properties, MIMO channel
properties, spectral efficiency, signal-to-noise ratio, data rates, and so
forth. Besides, it is relatively easy to include beyond 5G enabling
technologies such as mMIMO and reconfigurable intelligent surfaces (RIS) into
RT models. Considering the challenges presented by InF and IIoT scenarios, the
ray-based model offers an intriguing supplementary or alternative option to
empirical and stochastic methodologies.

The produced channel samples will be completed with additional scenarios and
made publicly available with free access soon.

## Acknowledgment

This work is part of a project that has received funding from the European
Union’s Horizon 2020 research and innovation programme under the Marie
Skłodowska Curie grant agreement No. 956670. We thank the Fraunhofer Heinrich
Hertz Institute’s support in understanding the measurement setup.

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