PREDICTIVE PATH LOSS MODELING IN 5G NETWORKS USING MACHINE LEARNING TECHNIQUES

The rapid increase in mobile users and data traffic has significantly influenced the evolution of wireless communication systems from 1G to 5G.In real-world urban environments, challenges such as "real call jumps" which lead to rapid signal fluctuations due to reflections and diffractions complicate reliable communication.Moreover, issues like channel propagation errors and overlapping communication paths degrade signal quality, resulting in increased path loss and a higher risk of communication failures.Currently, communication engineers at Base Stations (BS) and Mobile Switching Centres (MSC) primarily rely on traditional path loss calculation methods that focus on signal properties.However, these conventional techniques are becoming inadequate, particularly with the complexity and high frequency demands of 5G networks.They struggle with challenges such as synchronization errors, increased system complexity, and higher error rates.To address these limitations, a novel solution is proposed: leveraging machine learning to predict path loss more accurately.The foundation of this approach is the development of a "Path Loss Propagation" (PLP) dataset, which will be used to train machine learning models.The key goal is to build a system that can effectively predict future path loss values based on the input features within the PLP dataset.Python has been chosen for implementation due to its powerful ecosystem and rich set of libraries for machine learning.This solution aims to significantly improve the accuracy and efficiency of path loss prediction, ultimately supporting the development of more reliable and resilient wireless communication networks.