We are a group of people, we want to present this slide . I want you to divide it between us:
Mobile and sensor networks
ICT4SS 2022-2023
Deep Learning-Based
Path Loss Prediction
Model for 5G mmWave● Path loss: The reduction in power density of an electromagnetic wave as it
propagates through space.
● mmWave: A high frequency band of the electromagnetic spectrum used in
wireless communications, ranging from 30 to 300 gigahertz.
TERMINOLOGY
2Accurate Path Loss
Prediction in 5G
mmWave Networks
WHY?
Is important!
3Accuracy is essential for
Successful 5G mmWave
Deployment and Operation
Path loss varies due to
distance, antenna orientation,
and blockage by obstacles
ACCURACY VARIATION
COMPLEXITY
Traditional models may not
capture the mmWave
propagation complexity
OPTIMIZATION
Optimization of network
coverage, capacity, and QoS
| Importance of Accurate Path Loss Prediction in 5G mmWave Networks |
4Benefits of 5G
mmWave
communication
Smart Cities and
Connected
Communities
01 02
INTRODUCTION
03 04
Application
areas of 5G
Shortcomings
of 5G mmWave
communication
5Challenges in developing accurate path loss models for
wireless communication
Limitations of current models
Field campaigns
Cellular network planning
- Limitations of current models: limitations of current models,
including the difficulty of generalizing them to any propagation
environment and the high prediction errors that can arise when
deploying them in different environments.
- Field campaigns: Explain how field campaigns are used t characterize
the mobile radio channel and the challenges associated with collecting
sufficient data to develop accurate models.
6Path loss refers to the reduction in
power of a radio signal as it travels
through a wireless channel.
It is a natural phenomenon that
occurs due to various factors in the
environment, such as distance,
frequency, and obstacles.
What is path loss?
Source : Springer
71. Path loss impacts the coverage, capacity, and performance of wireless networks.
2. Proper understanding of path loss is essential for designing and optimizing wireless
communication systems.
3. Consideration of path loss helps ensure optimal network performance and coverage in
various scenarios.
4. Accurate path loss prediction is crucial for determining the required transmit power, link
budget, and antenna height in wireless networks.
5. Path loss affects network planning, deployment, and performance evaluation, making it
a key factor in wireless communication engineering.
6. Path loss models and prediction techniques play a crucial role in the design and
optimization of 5G mmWave networks.
Importance of Path Loss
8Path Loss Modelling
Path Loss Modelling as a
Regression Problem
Advantages of Artificial
Neural Networks
Limitations of
High-Dimensional Data
Deep Learning in
Path Loss Modelling
91. Background: Path loss prediction is critical for optimizing 5G mmWave communication
networks.
2. Traditional methods: Empirical and geometry-based stochastic models may have
limitations in accuracy and generalization.
3. Deep learning advantage: Deep learning models can overcome limitations by leveraging
large datasets and learning complex patterns from data.
4. Key areas of application: Wireless link quality estimation, multi-band path loss
prediction, and network performance optimization.
5. Steps in model design: Data collection, model training, and validation customized to
specific wireless communication environment.
Introduction to Deep Learning-Based
Path Loss Prediction Models
101. Accurate predictions: Deep learning models can analyse high-dimensional data and
establish relationships between various parameters for accurate path loss predictions.
2. Overcoming limitations: Deep learning models can overcome limitations of real-time
measurements and leverage historical data for improved predictions.
3. Complementary to conventional methods: Deep learning-based models can complement
existing optimization and planning tasks in 5G mmWave communication systems.
4. Enhanced network planning: Deep learning-based models enable more efficient and
optimized network planning and deployment.
5. Future prospects: Further research can contribute to the development of advanced
techniques for optimized network planning in 5G and beyond.
Benefits of Deep Learning-Based
Path Loss Prediction Models
11Regression Model
To map the interference
distance correlation with
path loss for optimal
prediction.
4-Layer Fully Connected
Classifier
Ray Tracing Technique
To compute the path loss
for an urban environment
and run simulations to
obtain data for designing
our classifier.
01
To predict the five classes of
path loss models discussed in
this paper.
02
Proposed Model Architecture
03
The proposed model architecture consists of three main components:
12• Deterministic Large-Scale Path Loss Model at 28 GHz
• 3D Map of Lagos Island, Nigeria to Analyze Path Loss
• The Benefits of Selecting the 28 GHz Carrier Frequency
• Study Area Overview: Coverage, Elevation, and Slope
• Setting up a Point to Multipoint Communication with Dipole Antennas
• Receiver Grid Setup and Data Collection
• Path Loss Variation with Respect to Distance
Proposed Model Architecture (Cont.)
13Dataset Preparation and Model Training
LOS
The simulation was carried
out for different scenarios,
including line-of-sight (LOS).
NLOS
non-line-of-sight (NLOS)
conditions, with varying
numbers of obstacles and
building heights.
The dataset used for model training and evaluation was generated using a ray-tracing
simulation tool.
14Dataset Preparation and Model Training (Cont.)
Training Set
Validation Set
Test Set
60% (53,310 samples)
20%
20%
- The proposed model was trained using the training set with various hyperparameters, including the
learning rate and batch size, and the model with the best validation performance was selected for
evaluation on the testing set.
- The model performance was evaluated using various metrics, including mean absolute error (MAE).
151. 2D feature vector dataset transformed
from path loss variations, trained with a
4-layer fully connected classifier.
2. Testing accuracy of 75.46% shows
improved performance with reduced
computation complexity and time
efficiency compared to empirical
methods.
Path Loss Classification with Deep Classifier
16● Regression model performance evaluated
using MAE loss function.
● Optimal setting achieved with a 4-layered
network.
● Model shows significant capability in fitting
data for different path loss categories.
● FSPL 1 and FSPL 2 exhibit increasing path
loss with distance.
Regression Analysis of Path Loss Prediction
17● Excess PL 1 and Excess PL 2 show high
randomness (non-linearity) due to 5G
network conditions, but regression model
performs well.
● Regression model capability: Significant in
fitting data for all path loss categories.
Regression Analysis of Path Loss Prediction
18● The potential applications of deep learning-based path loss
prediction models in 5G mmWave networks
● impact of accurate path loss prediction on network performance
and user experience
● Challenges and future research directions
Deep learning-based path loss prediction models in 5G mmWave networks
APPLICATIONS, IMPACT, and CHALLENGES
19• Optimize network coverage and capacity
• Improve quality of service for end-users
• Improve beamforming for better resource
utilization and interference management
• Radio resource management
(Selecting the best frequency band or adjusting transmit power
based on predicted path loss)
• Help identify coverage and interference
issues for network optimization
Deep learning-based path loss prediction
models in 5G mmWave networks
APPLICATIONS
20• USER EXPERIENCE:
o Accurate path loss prediction can optimize
network performance and improve user
experience in 5G mmWave networks.
● Efficiency and Management:
○ Traditional path loss models >> suboptimal network
performance in 5G mmWave networks
○ Deep learning-based models >> trained on real-world datasets
can enhance network coverage, capacity, and quality of service
by enabling efficient use of radio resources and better
interference management.
Deep learning-based path loss prediction
models in 5G mmWave networks
IMPACT
21• CHALLENGES:
o Lack of publicly available datasets that capture
the full range of real-world scenarios
o Need for real-time performance, which can be
limited by the computational complexity of
deep learning models
● FUTURE:
○ Reduce computational complexity of models
(By using pruning or quantization)
○ Test model performance in dynamic
environments
(e.g, Moving users and changing weather)
Deep learning-based path loss prediction
models in 5G mmWave networks
CHALLENGES
& FUTURE
22Data-driven approach: Highlight the fact that your approach is based on data-driven methods and
how this can be advantageous compared to traditional methods.
Complexity reduction: Emphasize the importance of complexity reduction and how you achieved this
by using a smaller number of feature vectors that are highly correlated.
Deep classifier and regression model: Explain how you used a deep classifier for path loss
classification and a regression model for path loss prediction. Highlight the benefits of using these
models.
Transfer learning: Discuss the concept of transfer learning and how you plan to use it in future work.
Explain how you will reuse the weights of your trained model for larger domain-data and complex
ray tracing
models.
Conclusion: Sum up the main findings of your study and the significance of your results. Highlight
how your work can contribute to the field and potential future directions for this research.
Conclusion
24Simon K. Hinga
Department of Electrical
and Computer Engineering
Santa Clara University
California, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Oluwaseun T. Ajayi
Department of Electrical
and Computer Engineering
Illinois Institute of Technology
Chicago, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Tokunbo Ogunfunmi
Department of Electrical
and Computer Engineering
Santa Clara University
California, USA
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
Reference
24
[ Deep Learning-Based Path Loss Prediction Model for 5G mmWave ]Thank You
“Brain Drainers” Group
Amirhossein Shekari
Mohammadsajjad Madadian
Kousha Nikkar
Maryam Bigonah
Mobile and sensor networks
ICT4SS 2022-2023
Deep Learning-Based
Path Loss Prediction
Model for 5G mmWave