I want you to make a recap of the IEEE article "Deep Learning-Based Path Loss Prediction Model for 5G mmWave". This recap must be fitted for a 20 minutes presentation. Moreover it must have the following specifications:
1. It must not maintain a very academic tone.
2. It must be structured and well sectioned
3. It must me memorizable 


Abstract—The proliferation of wireless devices that
utilize the 5G mmWave technology continues to stir
several design requirements for optimizing performance.
However, mitigating the underlying constraints posed
by path loss remains a challenging task. The use of
empirical methods and heuristic techniques have only
provided one-shot solution. We adopt a reusable learningbased method to alleviate the complexity in path loss
prediction. Our method involves the design of a deep
classifier and a regression model which learn relevant
network parameters from data and uses such knowledge
to differentiate different classes of path loss in a practical
5G radio environment. We show that deep learning
can alleviate the path loss modelling complexity in the
optimization of 5G networks.
Index Terms—deep classifier, path loss, mmWave, ray
tracing, regression
I. INTRODUCTION
Machine learning has a vast potential to solve socioeconomic challenges and transform business models
towards the realization of the 17 Sustainable Development Goals (SDGs) and the associated 169 targets
[1]. Governments and public administrators can exploit
the sizeable coherent 5G mmWave communication infrastructure and mobile connectivity ubiquity to realize
IoT-enabled Smart Cities and Connected Communities
[2]–[4]. 5G mmWave communication benefits include
underutilized bandwidth and small wavelength allowing spatial resolution and short transmission paths,
enhanced throughput, directional communication, high
channel capacity and low latency. Some notable application areas of 5G include scalable data usage,
low cost virtualized homes, smart grids, automation,
healthcare systems, logistic and tracking, IoT services
and industrial application [5]. The major shortcoming
of 5G mmWave communication is the high atmospheric
losses due to path loss, atmospheric absorption, rain
losses and foliage losses associated with the propagation terrain [6], [7].
While several research efforts [8]–[12] have been
made to develop accurate path loss models, the generalization of these models to any propagation environment is not sufficiently viable. Also, to accurately
characterize the environment, field campaign can take
longer time to record sufficient data to generalize the
model. Terrain peculiarities and unique clutter configurations of wireless channels give rise to high prediction
errors when deployed in a different environment other
than the one for which they were initially formulated.
Accurate characterization of the mobile radio channel
often requires thorough setting/tuning before adapting
to a new geographical environment and frequency [13].
In this case, cellular network planning will require
different propagation models for efficient radio network
performance. The need to predict signal attenuation
is paramount for better planning and implementation
of futuristic intelligent networks [14], [15]. Achieving
such an intelligent model requires conducting an extensive field campaign to collect big data on path loss that
can be used to develop a machine learning algorithm
that will intelligently analyse the relationship between
the data; such algorithm is a path loss prediction model.
In most cases, path loss modelling can be categorized as a regression problem due to the number of
parameters needed for accurate prediction. The use of
artificial neural networks [16] in path loss modelling
has posed a major breakthrough overcoming the shortcomings of geometry-based stochastic modelling and
time-consuming empirical models. Path loss prediction
depends on several parameters that artificial neural networks can fine-tune to develop a multi-band prediction
model. However, as the size of such parameter increases, and data become high-dimensional, developing
a robust classifier becomes a daunting task, while exploiting larger samples of data for path loss prediction.
Deep learning, a subset of machine learning, is hailed
for its powerful, intelligent learning and prediction
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2022 IEEE Global Humanitarian Technology Conference (GHTC) | 978-1-6654-5097-3/22/$31.00 ©2022 IEEE | DOI: 10.1109/GHTC55712.2022.9911009
Authorized licensed use limited to: Politecnico di Torino. Downloaded on March 30,2023 at 13:04:58 UTC from IEEE Xplore. Restrictions apply.capabilities. With deep learning, high-dimensional data
can be analyzed to establish a relationship between
various parameters. Several works have shown the
benefit of deep learning in path loss modelling. Most
works have used frequency-specific and measurement
campaign approaches in developing models to predict path loss [17], [18]. Also, parameter estimation
schemes which rely on existing empirical methods,
have been proposed to determine a nonlinear single
output variable that can accurately represent path loss
[19]. However, we opine that deep learning can serve
as a fast and optimal method for path loss prediction,
as knowledge extracted from data no longer rely on
the real-time physical-layer and channel condition of
the network. For example, measurement campaign is
constrained by dynamic environmental changes, which
cannot be undone each time such measurements are
taken. Also, the repeating of such process creates
bias in path loss modelling. Hence, path loss model
prediction using deep learning techniques can alleviate
the aforementioned challenges as data obtained from
past measurements can be used to train a classifier
by varying the input parameters. On the other hand,
a classifier which is trained on large data is strong to
predict the path loss, regardless of any environmental
effects caused by construction of new buildings or
growing of trees.
Some works in this direction include, wireless link
quality estimation and multi-band path loss prediction
model for future networks using deep learning [20]–
[22]. Despite the strength of deep learning in optimizing path loss prediction tasks, we emphasize that identifying the relevant parameters for path loss prediction
and reducing the complexity of a classifier design is
critical for improving the overall network optimization.
Our primary contribution is, thus, to complement the
conventional optimization and planning tasks in 5G
radio deployments, which is predicated on path loss
modelling. Our approach involves exploiting practical
application-domain data in 5G mmWave for path loss
prediction. Specifically, we study how the received
power and transmitter-receiver distance can be used
to predict expected path loss of a single receiver at
a point within a small cell. First, we applied a ray
tracing technique to compute the path loss for an urban
environment and run simulations to obtain data for
designing our classifier. Next, we designed a 4-layer
fully connected classifier to predict the five classes of
path loss models discussed in this paper. Finally, we
developed a regression model to map the interferencedistance correlation with path loss for optimal prediction.
The rest of this paper is organized as follows. We
describe the methodology in Section II. Next comes
the numerical results in Section III, and the conclusion
in Section IV.
II. METHODOLOGY
Simulation of path loss using ray tracing techniques
allows flexibility and controllability in changing parameters that are impossible to tune and calibrate during
field campaigns. Path loss modelling can be achieved
either via empirical models [23]–[25], or deterministic
methods [26]–[29]. In this work, we present a deterministic large-scale path loss model at 28 GHz using
a 3D map of Lagos Island, Nigeria, covering 6.271735
N to the north, 6.27437 N to the south, 3.235466
E to the southeast, and 3.234122 E to the west. In
our work, the 28 GHz carrier frequency was selected
as it provides a good trade-off between unfavourable
channel characteristics like path loss, rain fading, propagation loss, transmission through foliage loss, and
atmospheric absorption effect. The study area covers
560 meters with an average elevation of 2.4 meters,
elevation gain of 11.4 meters, a maximum slope of
5.7%-8.7%, and an average slope of 1.3%-0.9%. Four
half-wave dipole antennas were used to set up a point
to multipoint communication at a height of 20 meters,
the receiver grid was set up with 7,300 receivers spaced
at 5 meters with a height of 2 meters. From our field
data obtained from simulation, we observe the degree
of path loss variation with respect to distance, for five
different path loss categories which includes, non-line
of sight pathloss, free space path loss without antenna
pattern (FSPL 1), free space path loss with antenna
pattern (FSPL 2), excess path loss without antenna
pattern (Excess PL 1) and excess path loss with antenna
pattern (Excess PL 2).
Fig. 1 and Fig 2 (a) and (b) show the spatial
distribution for a non-line of sight path loss and the
excess path loss respectively; we observe the nonlinear relationship between distance and path loss.
Contrariwise, Fig. 3(a) and (b) shows a close-linearmapping between distance and path loss in free space.
The randomness in the non-line of sight path loss and
excess path loss is a function of the relative antenna
pattern as shown in the plots. Further, we summarize
these path loss categorical data in Table 1 following our
statistical analysis in obtaining the degree of dispersion
in the data.
III. RESULTS AND DISCUSSION
In this section, we analyse and present the results of
our deep learning approach in complementing the 5G
planning and optimization tasks for achieving reliable
traffic delivery in a path loss-constrained network.
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Distance (m) Non-LOS (dB) FSPL 2 (dB) FSPL 1 (dB) Excess PL 1 (dB) Excess PL 2 (dB)
count 936 936 936 936 936 936
mean 179.92 162.94 104.16 103.65 59.29 58.78
std 133.25 28.55 6.93 7.78 23.93 24.53
min 10.32 86.57 90.00 81.67 -0.55 -4.31
25% 73.66 149.01 99.07 98.75 46.87 46.25
50% 145.45 163.79 104.72 104.67 58.03 57.89
75% 246.65 179.53 109.29 109.27 72.57 72.30
max 545.38 244.12 116.21 116.20 139.75 139.67
Fig. 1: Non-line of sight path loss spatial representation
We employed a data-driven method by exploiting the
dataset from a 5G mmWave simulated network which
represents the mapping relationship between physicallayer network parameters. The path loss information in
the dataset, which is our parameter of interest, provides
relevant knowledge in identifying the type of antenna
to use for 5G deployment in a geographical area. The
dataset obtained from the network simulation spans
different path loss categories. As shown in Fig. 4, the
network layout is a typical grid of fixed transmitters
and receivers with uniform receiver distance of 5m and
communication range of 25m.
While exploiting deep learning methods to absorb
the intrinsic physical-layer parameters of the network,
we show that the knowledge extracted by our model
during the training phase can be transferred in solving
the prediction tasks of an entirely different sample,
while achieving close-to-optimal results. The metrics
used for performance evaluation of our deep classifier
and regression model are:
a) Accuracy (ACC): This is the ratio of correctly
predicted data points to the total data points in the
dataset. It depicts the overall strength of a classifier
in making predictions.
ACC = TP + FP TP ++ T N T N + F N  % (1)
(a) without antenna pattern
(b) with antenna pattern
Fig. 2: Excess path loss spatial representation
where TP and T N correspond to the true positive
and true negative prediction respectively; while FP
and F N refer to the false positive and false negative
prediction respectively.
b) Mean Absolute Error (MAE): The MAE is the
average difference between the true value and model’s
prediction. For our regression model, we use the MAE
as our metric for performance evaluation.
MAE = 1
N
NX i
=1
|yi,pred − yi,true| (2)
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(b) with antenna pattern
Fig. 3: Free space path loss spatial representation
Fig. 4: Simulation layout of transmitter-receiver grid
where yi,pred and yi,true are the predicted values, and
true values respectively, and N is the total number of
samples considered. We choose the MAE over mean
squared error (MSE) to reduce the effect of outliers in
the dataset.
A. Path Loss Classification with Deep Classifier
We consider a network setting where receivers are
positioned in cell grids and can receive traffic from
multiple transmitters. Next, we locally observe the path
loss variations under various antenna directivity, in
terms of line of sight (LOS) and antenna patterns at
different time-steps. The path loss dataset was transformed into a 2-dimensional feature vector (distance
and pathloss) and was used to train a multiclass classifier. The distribution of the samples is from five
classes vis-a-vis non-LOS, FSPL 1, FSPL 2, Excess `
PL 1 and Excess PL 2. In our design, a 4-layer fully
connected classifier was trained with 53,310 samples,
which was split into training and testing sets. The
SoftMax activation function and Adam optimizer were
used as the output layer function and learning algorithm
respectively.
Results from our experiments reflect a training accuracy of 70.95% and validation accuracy of 71.36%.
However, on the testing set, our classifier’s performance reached 75.46% accuracy which shows that
path loss prediction is a relatively complex task and
requires very large dataset to train. Considering the
computational complexity and the computation time
constraint posed by empirical methods, we opine that
deep learning can help achieve improved results with
reduced computation complexity and time efficiency.
Also, we emphasize that the sub-optimality of the
model in generalizing on the dataset stems from the fact
that the mapping relationship between the associated
distance and pathloss for some samples are highly
correlated. In our dataset, FSPL 1 and FSPL 2 have
a close match in data representation for many samples.
Fig. 5 shows the classifier performance on accuracy;
the validation curve depicts the instability in learning
the dataset, which is as a result of high data correlation
among inter-class samples.
Fig. 5: Path loss prediction using deep classifier
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As earlier mentioned, we intend to show the mapping
relationship between the different path loss values and
the underlying physical-layer parameters. For reliable
and optimal prediction of path loss values, we developed a regression model to fit feature vectors, that is,
the channel-state information (CSI-SINR) and distance
to their associated path loss values. Our idea is to
identify how well these feature vectors provide accurate
path loss values based on the training of a regression
model. Since our dataset consists of continuous random
variables, a 4-layer fully connected regression model
was trained using a hyperbolic tangent (tanh) function
in the hidden layers. The output layer consists of
5 neurons, each, with a linear activation function to
estimate the path loss value for each category of path
loss.
To evaluate the performance of the regression model,
the MAE loss function is used to compute the prediction error. In our experiment, we employed different
hyperparameter tuning methods in building a strong
regression model, such as decreasing and increasing
the number of layers. For example, using a 3-layer or
5-layer network, causes the model to overfit the data;
our optimal setting is a 4-layered network. The results
presented for our experiments are from the testing
sets which are not part of the training samples. To
visualize the performance of our regression model, with
respect to the MAE, we use graphs to clearly show
the predicted value and target/true value of the path
loss for each category as shown in Fig. 6 - Fig. 8.
We observe that for FSPL 1 and FSPL 2, the path
loss increases with distance, but for other path loss
categories such as the non-LOS, Excess PL 1 and
Excess PL 2, there is a high degree of randomness
(non-linearity), which is the case for a practical 5G
network where signals suffer from fading, multipath
reception and atmospheric absorption. However, our
regression model shows significant capability in fitting
the data for all the path loss categories. The prediction
error as a function of MAE is as a result of the high
degree of correlation between the input vectors, that is,
the samples have low degree of dissimilarity for two
pair of path loss categories, e.g FSPL 1 and FSPL 2,
and Excess PL 1 and Excess PL 2 in Fig. 7 and Fig.
8 respectively.
This further shows consistency with our model’s accuracy in Section IV-A where performance is affected
by the high-level similarity in the samples of different
path loss categories. Fig. 9 compares the MAE path
loss score for each category; it depicts the proportional
error during training and testing. From the plot, both
FSPL 1 and FSPL 2 have the lowest MAE score owing
to the fixed pattern (linearity) in the data as can be
seen, unlike for other categories where stochasticity is
evident.
Fig. 6: Non-LOS path loss prediction using linear
regression model
(a) FSPL 1
(b) FSPL 2
Fig. 7: Free space path loss prediction using linear
regression model
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(b) Excess PL 2
Fig. 8: Excess path loss prediction using linear regression model
Fig. 9: Evaluation of the regression model using MAE
IV. CONCLUSION
In this paper, we present a data-driven approach
in solving path loss prediction tasks. Specifically, we
focus on complexity reduction by using fewer number
of feature vectors that are highly correlated to train
a deep classifier for path loss classification, and a
regression model for path loss prediction. The concept
of transfer learning, by reusing the weights of our
trained model for larger domain-data and complex ray
tracing models is considered as a future direction for
this work.
V. ACKNOWLEDGEMENT
The research work in this paper was supported in
part by the David Packard Endowed Faculty Fellowship
at Santa Clara University