- Model Development and Comparison:
Objective: Develop predictive models for path loss using machine learning or deep learning algorithms. Compare traditional path loss models (like Hata-Okumura, COST231, etc.) with your machine learning models to evaluate improvements in prediction accuracy. Contribution: This could show how advanced data-driven techniques can outperform traditional models in certain environments or under specific conditions.
- Environment-Specific Modeling:
Objective: Create separate predictive models for different environments (rural, urban, suburban) considering the unique propagation challenges in each. Investigate how environmental features (like building density, vegetation, and terrain) influence path loss. Contribution: Provides insights into environment-specific factors affecting path loss and enhances the precision of predictions in varied contexts.
- Impact of Weather and Obstacles:
Objective: Analyze the impact of weather conditions and obstacles on path loss, especially focusing on NLOS scenarios. Use the dataset to understand how rain, fog, and other conditions alter signal attenuation. Contribution: Offers detailed analysis on less-studied factors affecting path loss, contributing to the design of more robust communication systems.
- Frequency Band Analysis:
Objective: Investigate how different frequency bands (considering the dataset spans a range of frequencies) affect path loss, particularly with the advent of 5G and beyond where higher frequency bands are in use. Contribution: This could help in optimizing frequency band usage for future wireless networks, ensuring efficient spectrum utilization.
Path Characteristics and Shadowing Effects:
Objective: Explore the relationship between path characteristics (LOS, NLOS), shadowing effects, and path loss. Develop models that can accurately predict path loss by incorporating these parameters. Contribution: Enhances understanding of complex propagation mechanisms in urban environments, improving prediction models for dense cityscapes.
Data-Driven Optimization for Network Planning:
Objective: Use the dataset to perform data-driven optimization for network planning and placement of base stations. Incorporate variables like transmitter/receiver height and antenna gain into the optimization model. Contribution: Provides a framework for optimizing wireless network design based on empirical data, potentially reducing deployment costs and improving coverage.