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# Simulation-Enhanced Data Augmentation for Machine Learning Pathloss
Prediction

Ahmed P. Mohamed1, Byunghyun Lee1, Yaguang Zhang1, Max Hollingsworth2, C.
Robert Anderson3,
James V. Krogmeier1, David J. Love1  This work is supported by the National
Science Foundation under grants EEC-1941529, CNS-2212565, and CNS-2225578.
(Ahmed P. Mohamed and Byunghyun Lee contributed equally to this work.) Email:
{mohame23, lee4093, ygzhang, jvk<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>1Elmore Family School of Electrical and Computer Engineering,
Purdue University, West Lafayette, IN 47907
2University of Colorado, Boulder, CO 80309
3Bradley Department of Electrical and Computer Engineering, Virginia Tech,
Blacksburg, VA 24061

###### Abstract

Machine learning (ML) offers a promising solution to pathloss prediction.
However, its effectiveness can be degraded by the limited availability of
data. To alleviate these challenges, this paper introduces a novel simulation-
enhanced data augmentation method for machine learning (ML) pathloss
prediction. Our method integrates synthetic data generated from a cellular
coverage simulator and independently collected real-world datasets. These
datasets were collected through an extensive measurement campaign in different
environments, including farms, hilly terrains, and residential areas. This
comprehensive data collection provides vital ground truth for model training.
A set of channel features was engineered, including geographical attributes
derived from LiDAR datasets. These features were then used to train our
prediction model, incorporating the highly efficient and robust gradient
boosting ML algorithm, CatBoost. The integration of synthetic data, as
demonstrated in our study, significantly improves the generalizability of the
model in different environments, achieving a remarkable improvement of
approximately $12\text{\,}\mathrm{dB}$ in terms of mean absolute error for the
best-case scenario. Moreover, our analysis reveals that even a small fraction
of measurements added to the simulation training set, with proper data
balance, can significantly enhance the model’s performance.

##  I Introduction

Radio signals experience pathloss as they propagate to a receiver. Pathloss
refers to the attenuation of the signal of a communication link between the
transmitter and the receiver. Accurately estimating pathloss is fundamental
for coverage estimation and interference analysis, which are key to effective
network planning. Additionally, accurate pathloss prediction enables improved
mobility management, such as handover and quality-of-service prediction.
However, predicting pathloss is challenging because it depends on various
factors, including propagation distance, geometry (e.g., buildings and trees),
antenna pattern and carrier frequency [1].

The fundamental model for the prediction of pathloss is the Friis equation,
which calculates the loss of transmission in the free space based on the
distance and carrier frequency [2]. Terrain-based models improve accuracy by
incorporating topological features. The irregular terrain model (ITM), often
referred to as the Longley-Rice model, aims to predict pathloss considering
terrain profiles [3]. The extended Hata (eHata) model adds environment
categories, such as urban, suburban or rural settings, to account for
different endpoint propagation environments [4]. However, in many practical
scenarios where multiple environments are combined, these models may fail to
provide an accurate pathloss prediction.

Stochastic models add a random variable to a deterministic pathloss model to
describe the randomness (e.g., scattering and multipath effect) in a wireless
link. The most widely used model is the log-normal shadowing model, which
accounts for shadowing with a Gaussian random variable. Furthermore, the COST
231 model [5], 3GPP spatial channel model [6], and QuadRiGa [7] empirically
model the shadowing distribution. These models are useful in that they can be
easily employed without high complexity and provide a general understanding of
signal propagation characteristics in various environments. However, since
these models do not consider the exact surroundings and environments of the
transmitter and receiver, they may not capture site-specific features.

Ray-tracing models simulate the propagation of electromagnetic waves using
deterministic modeling of reflection, diffraction, and scattering [8, 9]. Ray-
tracing has been used extensively for research purposes as it offers detailed
information about the structure of a wireless channel, including angle of
arrival, angle of departure, and path delays. Despite this, the computational
cost of ray-tracing becomes unaffordable as the simulation scale increases,
making it less suitable for large-scale implementations.

To overcome these issues, learning-based models have attracted significant
research interest [10, 11, 12, 13, 14]. Despite their ability to learn site-
specific features, the prediction performance of learning-based models is
largely based on the availability of extensive high-quality data. Nonetheless,
the collection of such datasets poses challenges as it involves considerable
human labor and sophisticated measurement tools. Although driving tests are
often used to streamline this process, they are not practical in locations
such as farms and school campuses, which can bias the data sets to publicly
accessible roads. Due to the lack of data, a learning-based prediction model
may struggle to generalize these unfamiliar radio environments. This is a
major issue for rural wireless communication applications, where diverse
propagation environments coexist in a wide area [15].

In the machine learning (ML) domain, simulation-assisted data augmentation has
been actively investigated due to its cost-effectiveness and convenience [16,
17]. However, the use of simulation-aided data augmentation remains relatively
unexplored in the domain of wireless communications, and more specifically, in
pathloss prediction. In [18], the authors used simulation data to cover
inaccessible geographic regions for site-specific channel modeling. However,
this work focused on augmenting a partial dataset for a single geographic area
rather than augmenting a dataset with different environments.

In this paper, we introduce a novel data augmentation method for ML pathloss
prediction, which incorporates both real and synthetic data. Our goal is to
improve the generalizability and reliability of the pathloss prediction model
by enriching the dataset with synthetic data, especially in cases where
measurement data are limited. The proposed data augmentation, as shown in Fig.
1, has three main components: (i) measurement data collection, (ii) synthetic
data generation, and (iii) feature extraction. For the collection of
measurement data, we conducted a measurement campaign in three different
environments (rural, residential, and hilly). We then produce synthetic data
using the state-of-the-art large-scale pathloss simulator [19]. This simulator
utilizes high-resolution LiDAR data to extract features along line-of-sight
(LoS) paths, enabling the prediction of large-scale fading. We then develop
features based on domain knowledge and extract them from LiDAR-based
geographic datasets. We evaluated the prediction performance of the augmented
dataset in various scenarios. The results show that the combination of
synthetic and real data can improve the prediction performance for unseen
environments at a slight cost of loss of accuracy for known environments.

##  II Simulation-Enhanced Data Augmentation

Learning-based models often suffer from inaccurate predictions in unfamiliar
or unseen environments. To address this problem, this paper aims to build a
robust model that can perform well not only for known areas but also for
unseen scenarios by incorporating synthetic data. Specifically, we detail the
data processing procedure for merging synthetic data with measurements, as
well as the data collection procedure. In this section, we will outline our
proposed data augmentation methodology and then describe the procedures for
creating both measurement and synthetic datasets.

Figure 1: Flowchart of the proposed simulation-enhanced data augmentation
process.

###  II-A Methodology

Fig. 1 illustrates the overall flow of the proposed data augmentation. The
three main components of the proposed simulation-enhanced data augmentation
can be described as follows:

  * •

Measurement Data Collection: Measurement data are collected from mobile phones
in different environments. These measurements can be used to obtain pathloss
values and associated data, such as location, cell information, and carrier
frequency.

  * •

Synthetic Data Generation: The simulation parameters such as user location and
carrier frequency should be determined based on what the measurement dataset
lacks, to enhance the dataset. For example, if the dataset lacks rural
environment data, simulations for rural environments can be performed using
rural geographic data and macrocell parameters.

  * •

Feature Extraction: After preparing the measurement and synthetic datasets, we
extract the features of the data points from the geographic datasets, which
are predominantly site-specific. We carefully design features that reflect the
geometry and surroundings of the transmitter and receiver using domain
knowledge. Then, we generate features for every geographic point in the area
of interest and pair them with the corresponding pathloss values collected in
the previous step.

###  II-B Creating Measurement Datasets

In this section, we will describe how we collected datasets in different
environments and the process of converting the Reference Signal Received Power
(RSRP) measurements into path loss values.

####  II-B1 Collecting RSRP Measurements

We carried out a comprehensive data collection in the city of West Lafayette,
located in the state of Indiana in the USA. We collected data from three
different environments: farm, hilly and residential areas, as illustrated in
Fig. 2. We conducted 4G LTE measurements using three commodity Android phones,
Samsung Galaxy S8, S20 and S21. In addition, we used a cellular network
monitoring app called G-NetTrack. Our measurements include RSRP measures, cell
identifiers (IDs), E-UTRA Absolute Radio Frequency Channel Number (EARFCN),
and Global Positioning System (GPS) coordinates. Note that EARFCN represents
the LTE band and the center frequency of the serving cell. To acquire cell-
site information, we used a mobile application called CellMapper [20] and a
database known as Antenna Search [21]. CellMapper is a crowd-sourcing
application that offers useful cell-specific information, such as cell types,
cell IDs, uplink/downlink carrier frequency, and cell addresses. We determined
the addresses of the serving cells using the cell IDs from our measurements.
However, since CellMapper only provides approximate addresses, we took an
additional step using Antenna Search. Specifically, we searched for the
approximate addresses obtained from CellMapper on Antenna Search and extracted
detailed tower information such as GPS coordinates and tower height.

(a) Rural (ACRE)

(b) Residential (Lindberg)

(c) Hilly (Happy Hollow)

Figure 2: RSRP in $\mathrm{dBm}$ derived from the data collected during the
measurement campaign. Figure 3: Illustration of the engineered features used
as inputs for ML algorithm.

####  II-B2 Converting RSRP into Pathloss

In 4G LTE, RSRP is the average power received in the resource elements (RE)
that carry cell-specific reference signals (CRS). The RSRP in $\mathrm{dBm}$
can be expressed as [19, 22]

$\begin{split}\lx@glossaries@gls@link{acronym}{rsrp}{\leavevmode
RSRP}=-PL+\Delta\,\end{split}$ (1)

where $PL$ is the pathloss in $\text{\,}\mathrm{dB}$ and $\Delta$ is an
offset. The offset $\Delta$ represents the cumulative effect of unknown site-
specific parameters, including transmit power, antenna gain, and cable loss.
Since these parameters are in general not available, we have to estimate the
offset $\Delta$ of each site to compute the path loss. To this end, we
compared RSRP in the measurements and pathloss in the synthetic data point-to-
point. The difference between the $\ell$th RSRP measure and its corresponding
simulated point can be written as $\Delta_{\ell}$. Then, the offset $\Delta$
can be obtained by taking the average of the differences as

$\Delta=\frac{1}{N_{s}}\displaystyle\sum_{\ell=1}^{N_{s}}\Delta_{\ell},$ (2)

where $N_{s}$ is the number of RSRP samples. In practice, the averaging
process can cause additional errors. Therefore, it would be desirable to
derive the exact offsets if site-specific parameter information is available.
Once the offsets are calculated, the pathloss can be derived by subtracting
the values of RSRP from the offsets, that is,
$PL=\Delta-\lx@glossaries@gls@link{acronym}{rsrp}{\leavevmode RSRP}$.

###  II-C Creating Synthetic Datasets

We utilized a comprehensive cellular coverage simulator [19] to estimate
generate pathloss for various scenarios, based on high-accuracy LiDAR data
with meter-level resolution. The simulation area for each scenario was
designed to cover all relevant measurement points and their surrounding
environments. Typical LTE network parameters and equipment specifications were
applied (Section II-B2).

As introduced in [19], two sets of simulation results are available for
analysis: pathloss based on the eHata model and cumulative blockage distance
based on the 60% clearance test of the first Fresnel zone. The locations of
the cell towers were determined by a manual process that involved cross-
referencing of data from various publicly accessible tools and sources
(Section II-B). All carrier frequencies for each scenario, as observed in the
corresponding measurement data set, were included in the simulation.

##  III Feature Extraction

In this section, we develop and extract the features of the pathloss
prediction model. Given the significant impact of feature engineering on ML
prediction performance, we carefully define and extract geographical features
using geographical datasets.

###  III-A Geographic Dataset

To extract geographic features from the LiDAR datasets, we used Indiana’s
statewide LiDAR datasets collected in 2018 and Digital Surface Model (DSM) and
Normalized Digital Height Model (NDHM) for geographic profiles [23]. The DSM
provides the elevation above sea level for each point, representing the
vertical height of the tallest objects, including trees and buildings at those
points. In contrast, NDHM provides information about the elevation above
ground level, which indicates the height of the clutter (e.g., buildings and
trees) at each point. The DSM and NDHM have a spatial resolution of 5 feet.
With these two datasets, we can determine the elevation of the ground and the
height of the surface of each point.

###  III-B Feature Engineering

For radio attributes, we focus on the center frequency, given its significant
influence on pathloss [1]. In terms of geographic attributes, we have
incorporated both endpoint- and path-based characteristics, including the
relative height of the serving cell to each reception point and the elevation
angle. Fig. 3 illustrates the radio and geographical features. The description
of the features is given below.

  * •

Carrier Frequency: Represents the center frequency at which the communication
signal is transmitted. This parameter can be extracted directly from the
attribute EARFCN in the measurement dataset.

  * •

Serving base stations (BS) Distance ($d_{BS}$): Defined as the distance
between a receiving point and its service BS.

  * •

Relative BS Height ($H_{BS}$): The height of the serving BS relative to a
receive point.

  * •

Average Clutter Height ($H_{C}$): Average relative height of clutter of
neighboring points. Neighboring points are defined as points within a radius
circle $R$ centered on a received point. In this study, we choose to use
$R=$50\text{\,}\mathrm{m}$$.

  * •

Terrain Roughness: This parameter characterizes the roughness of the terrain,
which is defined as the difference between the top $10\%$ and $90\%$ ground
elevation of the neighboring points.

  * •

Transmitter Height Above Average Terrain (TxHAAT): The difference between the
BS height and the average clutter height. This parameter provides information
about the height of BS above the surrounding terrain.

  * •

Ratio $\bm{\alpha}$ [12]: The ratio $\alpha$ is defined as
$\frac{H_{BS}-H_{C}}{d_{BS}}$. This ratio is used to characterize the
elevation angle between the serving BS and a receiving point.

##  IV Performance Evaluation

For the performance evaluation, collected measurements were used from three
different areas. The collected datasets are composed of about 133,800 RSRP
measurements, spanning three different scenarios:

  * •

ACRE (71,068 samples): Rural farm area

  * •

Lindberg (16,107 samples): Residential area.

  * •

Happy Hollow (46,641 samples): Hilly suburban area

We created simulations for ACRE, Lindberg, and Happy Hollow based on the cell
information acquired from the measurement campaign. We configured user
location grids with 6300 points for ACRE, 9200 points for Lindberg, and 5900
points for Happy Hollow to fully cover the area of interest. We simulated each
site with varying center frequencies from $731.5\text{\,}\mathrm{MHz}$ to
$2538.2\text{\,}\mathrm{MHz}$ to account for different propagation
characteristics. In the end, we obtained a synthetic dataset of approximately
651,000 data points, which improved our training datasets with different
environments.

To evaluate the prediction performance of the ML models, we use mean absolute
error (MAE), which is expressed as

$MAE=\frac{1}{n}\sum_{i=1}^{n}|L_{i}-\hat{L}_{i}|$ (3)

where $n$ is the number of pathloss samples, $L_{i}$ is the true pathloss, and
$\hat{L}_{i}$ is the predicted pathloss.

Figure 4: Prediction performance in the same environment, where training data consists of synthetic data structured to mimic the characteristics of the real test data environment. Training Data | MAE [dB] for Different Testing Datasets
---|---
Scenario 1 | ACRE | Happy Hollow
ACRE (R) + Happy Hollow (S) | 4.97 | 7.7
ACRE (R) | $4.33$ | $8.7$
Happy Hollow (S) | $8.6$ | ${7.98}$
Happy Hollow (R) + ACRE (S) | 8.74 | 5.55
Happy Hollow (R) | $9.65$ | $5.22$
ACRE (S) | $8.74$ | $6.59$
Scenario 2 | Lindberg | ACRE
Lindberg (R) + ACRE (S) | 6.32 | 8.75
Lindberg (R) | $5.39$ | $10.49$
ACRE (S) | $10.14$ | $8.74$
Scenario 3 | Lindberg | Happy Hollow
Lindberg (R) + Happy Hollow (S) | 9.5 | 7.79
Lindberg (R) | $5.39$ | $19.99$
Happy Hollow (S) | $10.23$ | ${7.98}$
Table I:  Pathloss prediction accuracy (in MAE) comparing the proposed data
augmentation (highlighted) to baselines. ”R” denotes real data and ”S” denotes
simulated data. For each dataset, training includes all synthetic data and 50%
of the real data, the remaining real data being used for evaluation.

###  IV-A Comparison with Empirical Radio Propagation Models

We compared our ML-based pathloss prediction with traditional empirical
models111The source code for this work is available publicly at
https://github.com/aprincemohamed/DeepLearningBasedCellularCoverageMap . that
use an equation to calculate pathloss at any given location from the base
station [24]. Specifically, we used the COST-231 Hata model and the Stanford
University Interim (SUI) model as benchmarks [25]. The COST-231 Hata model
includes adjustments for different environments, such as urban, suburban, and
rural areas. The SUI model refines the predictions by offering correction
factors for antenna heights of the base station and user equipment, along with
constants that vary depending on the type of terrain. We applied terrain A
corrections for the Lindberg and Happy Hollow datasets and terrain C
corrections for the ACRE dataset, as specified for the SUI model [25].

Fig. 4 compares the prediction performance of the deterministic and ML models
at different sites. We evaluated several ML algorithms, including Random
Forest, AdaBoost, HuberRegressor, and CatBoost, to identify the one that
offers the highest accuracy. The models were trained on a synthetic dataset
and then validated against a real-world dataset. Specifically, for the Happy
Hollow datasets, excluding the center frequency feature from training and
testing improved the model’s performance over using all features.

In general, it can be verified that CatBoost outperforms the other ML schemes
and empirical models. The MAE achieved for the ACRE, Happy Hollow, and
Lindberg data sets was approximately $8.76\text{\,}\mathrm{dB}$,
$8.02\text{\,}\mathrm{dB}$, and $9.88\text{\,}\mathrm{dB}$, respectively. It
should be noted that synthetic data, coupled with the developed features,
contributed significantly to favorable results. This is evidenced by
consistently lower MAE values, which significantly undercut those obtained
with empirical models. For the Happy Hollow dataset, the COST-231 Hata model
achieved an MAE of $6.8\text{\,}\mathrm{dB}$, which is marginally better than
the $8.02\text{\,}\mathrm{dB}$ of CatBoost when using synthetic data. However,
when CatBoost is trained on real data, specifically with a 50% split, it
exhibits superior performance with an MAE reduced to approximately
$5.22\text{\,}\mathrm{dB}$, outperforming the COST-231 Hata model. We infer
that CatBoost’s superior performance can be attributed to its sophisticated
ordered-boosting technique, which is especially effective in avoiding
overfitting. Hence, we chose CatBoost as our main ML scheme for the following
experiments.

These comparisons clearly highlight the advantage of using machine learning
over empirical models. This approach circumvents the need for costly data
collection processes by showing that our models, when trained with cost-
effective synthetic data, still maintain high performance in real-world
scenarios. This is particularly advantageous because it is achieved without
reliance on complex or expensive data types, such as images, highlighting the
practicality and efficiency of our methodology.

###  IV-B Generalization Performance Evaluation

In Table I, we assess how effectively our proposed training method generalizes
across different scenarios, specifically comparing its performance when using
only measurement data versus scenarios that use only synthetic datasets. For
the measurement data at each location, we partitioned the dataset into two
equal halves, allocating 50% for training purposes and the remaining 50% for
testing. Generally, cases that use only measurement data show good prediction
accuracy in known areas; however, their performance decreases in unseen areas.
In all scenarios, the cases that used only measurement data showed lower
performance compared to those that only used simulation data to predict unseen
areas. It can be seen that the proposed training method improved the accuracy
of the prediction of unseen areas by incorporating synthetic data, although at
the cost of some accuracy in known areas. Specifically, the ACRE (R) + Happy
Hollow (S) case improved the prediction accuracy of Happy Hollow by
$1.0\text{\,}\mathrm{dB}$ compared to the ACRE (R) case. On the other hand,
the accuracy of the ACRE prediction decreased slightly by
$0.64\text{\,}\mathrm{dB}$. Similarly, the Happy Hollow (R) + ACRE (S) case
improved the prediction accuracy of ACRE by $1.09\text{\,}\mathrm{dB}$, with a
minor loss of the prediction accuracy of $0.33\text{\,}\mathrm{dB}$ for Happy
Hollow. A similar trend can be observed in Scenario 2. The proposed data
augmentation improved the prediction accuracy for ACRE by
$2.26\text{\,}\mathrm{dB}$ at the cost of a $1.07\text{\,}\mathrm{dB}$
accuracy loss for Lindberg.

In Scenario 3, the only real data-based scheme achieves the prediction
accuracies of $19.99\text{\,}\mathrm{dB}$ and $5.38\text{\,}\mathrm{dB}$ for
Happy Hollow and Lindberg, respectively. The large difference in the
prediction results for Happy Hollow (hilly) and Lindberg (residential) may
suggest that they have significantly different propagation environments.
However, when the synthetic data for Happy Hollow was added to the dataset,
the prediction accuracy for Happy Hollow was enhanced by approximately
$12.2\text{\,}\mathrm{dB}$. This improvement came with a trade-off, leading to
a loss $4.11\text{\,}\mathrm{dB}$ in the accuracy of the Lindberg prediction,
which is consistent with previous findings.

These results suggest that the incorporation of synthetic data into training
improves prediction accuracy in almost all test scenarios, as seen in the
lower dB values for the combinations of real and synthetic data compared to
real data alone or synthetic data alone. This aligns with our earlier
discussion on the potential advantages of leveraging synthetic data to address
the generalization challenge posed by data scarcity.

Figure 5: MAE vs 5% Real Data Repetitions in Training Set.

###  IV-C Training Optimization with Limited Measurement Data

Next, we evaluate the prediction performance when available measurement data
is limited. In real-world applications, the challenge of data scarcity is
common. To navigate this, we use a nominal 5% of available measurement data in
the training process. To evaluate the impact of simulation-aided data
augmentation, we compared our proposed method with a baseline that uses only
measurement data. In our approach, for each site, we randomly sampled only 5%
of the entire measurement dataset for training and used the remaining $95\%$
for testing. To counterbalance the limited measurement data, we augment it
with synthetic data, exposing the model to a broader range of scenarios. The
synthetic dataset, which is significantly larger than the real data, risks
overfitting the model to its characteristics. To address this issue, we
implemented a strategy of repeating the measurement dataset multiple times in
each epoch, ensuring a balanced distribution of real and synthetic data during
the model training phase.

Fig. 5 shows the prediction accuracy for different scenarios with varying
numbers of repetitions. Clearly, our approach, which involves repeated
exposure to a small subset of measurement data, results in significant
improvements in predictive accuracy. Specifically, for ACRE datasets,
employing 20 repetitions of the 5% real data subset, the model achieved an MAE
of $5.05\text{\,}\mathrm{dB}$, a significant improvement over the MAE of
$8.77\text{\,}\mathrm{dB}$ observed when the model was trained only using
synthetic data. Interestingly, the figure also highlights the benefits of our
repetitive method compared to using only real data without repetition, which
yielded a slightly higher MAE of $5.11\text{\,}\mathrm{dB}$ using 16
repetitions. Similarly, for the Happy Hollow site, the MAE was reduced to
$5.67\text{\,}\mathrm{dB}$ with 20 repetitions, compared to
$7.72\text{\,}\mathrm{dB}$ using only synthetic data and slightly better than
$5.74\text{\,}\mathrm{dB}$ observed with 18 repetitions of only real data.
Furthermore, for the Lindberg dataset, where the model reported an MAE of
$6.25\text{\,}\mathrm{dB}$ with 10 repetitions, significantly outperforming
the MAE of the synthetic data of $9.87\text{\,}\mathrm{dB}$, and also showing
a slight advantage over the MAE of $6.32\text{\,}\mathrm{dB}$ achieved with 10
repetitions of only real data.

By addressing data imbalance, we improved the prediction accuracy of our
model, even with limited measurements in training sets. This underscores the
efficacy of our simulation-aided data augmentation, which proves its potential
as a solution to measurement scarcity in real-world applications. Our approach
enhances performance beyond what is achieved with either synthetic or real
data alone. It showcases the advantages of a balanced mix of both, thus
optimizing predictive accuracy across various datasets.

##  V Conclusion

This paper addressed a simulation-enhanced data augmentation method to
overcome the data shortage problem in the prediction of pathloss via ML. We
collected data for three different environments using commodity mobile phones
and augmented the dataset using synthetic data to enhance diversity. For the
ML prediction model, we engineered and extracted geographic features from
LiDAR datasets. We conducted a rigorous performance evaluation to show the
effectiveness of the proposed method. The experimental results demonstrated
that the proposed data augmentation method effectively enhances the
generalization performance of the prediction model. In summary, our research
offers a practical and effective strategy for the prediction of pathloss,
highlighting the potential of simulation-aided ML to mitigate data scarcity
challenges and improve the efficiency of network deployment.

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