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Isabona, Joseph
- Hybrid Empirical and Machine Learning Approach to Efficient Path Loss Predictive Modelling: An Overview
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Authors
Affiliations
1 Federal University Lokoja/Department of Physics, Lokoja, NG
2 Department of Physics, Federal University Lokoja, Lokoja, Kogi State, NG
3 Department of Computer Science, Federal University Lokoja, NG
4 Department of Physics, Delta State College of Education, Mosogar 331101, NG
1 Federal University Lokoja/Department of Physics, Lokoja, NG
2 Department of Physics, Federal University Lokoja, Lokoja, Kogi State, NG
3 Department of Computer Science, Federal University Lokoja, NG
4 Department of Physics, Delta State College of Education, Mosogar 331101, NG
Source
International Journal of Advanced Networking and Applications, Vol 15, No 3 (2023), Pagination: 5931– 5939Abstract
In the field of wireless communication and network planning, accurate path loss predictive modelling plays a vital role in understanding the behavior of signal propagation in diverse environments. Traditional empirical models have been widely used for path loss estimation, but they often lack the flexibility to adapt to complex scenarios. On the other hand, machine learning techniques have shown great potential in various domains, including wireless communication. This paper aims to present a hybrid empirical and machine learning approach for efficient path loss predictive modelling. By combining the strengths of empirical models and machine learning algorithms, we can enhance the accuracy and adaptability of path loss predictions. The following sections provide an overview of path loss modelling, explore traditional empirical techniques, discuss the application of machine learning approaches, and outline the methodology for the hybrid approach, along with evaluation and analysis. Finally, we conclude with a summary of findings and suggest future directions for research in this field.Keywords
Network planning, Accurate predictive modelling, Signal propagation, Empirical models, Machine learning models.Full Text
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