Modeling Received Power from 4G and 5G Networks in Greece U sing Machine Learning

Wireless propagation modeling is crucial for designing 5G networks and deploying base stations. Traditional models are constrained by different propagation environments, and deterministic models using ray tracing demand extensive computational resources. In recent years, advances in data-driven artificial intelligence (AI) have significantly improved the fitting of intelligent propagation models for 5G systems. Using artificial intelligence (AI) and substantial measured data, we conduct a comparative study of various machine learning (ML) models to perform accurate regression predictions for Reference Signal Receiving Power (RSRP). The results of the proposed ML models are compared and analyzed, exhibiting great accuracy in the prediction of RSRP values in a diverse range of urban, suburban, and rural environments.