Exploratory Data Analysis for Path Loss Measurements: Unveiling Patterns and Insights Before Machine Learning

Abstract

This paper presents an in-depth investigation into path loss modeling for wireless communication systems, emphasizing the critical role of Exploratory Data Analysis (EDA) and accurate field data collection. It reveals the enhanced efficacy of theoretical models when synergized with specific, real-world data, highlighting the necessity for empirical grounding. Central to our research is the demonstration of the indispensability of thorough EDA in path loss modeling. This phase uncovers key trends and anomalies, setting a solid foundation for subsequent Machine Learning (ML)-based predictive modeling. Deep dataset insights allow for the effective tuning of algorithms to reflect the complexities of signal propagation environments accurately. The study further underscores the impact of the quality of field data collection on model accuracy. Meticulous data gathering that accurately mirrors environmental conditions is essential, providing insights into signal behavior and validating theoretical models. Our research also explores the importance of spatial pattern analysis in understanding path loss. Advanced visualization and clustering methods are employed to illustrate how spatial variability significantly influences path loss, shedding light on the effects of environmental factors like obstructions and topography on signal propagation.

Keywords

• 5G
• mmWave
• path loss
• wireless communications
• exploratory data analysis
• machine learning