Enhancing Reference Signal Received Power Prediction Accuracy in Wireless Outdoor Settings: A Comprehensive Feature Importance Study

Predicting the reference signal received power (RSRP) in wireless communication is crucial for improving network performance, allocating resources, and ensuring good signal coverage, especially in advanced technologies like 5G and beyond. To create an accurate prediction model, we need to look at different aspects of the radio environment and understand the importance of each factor. In our study, we suggest using machine learning (ML) to predict RSRP. We analyze the importance of features by studying their impact on the received signal power. We developed an ML approach using 64 features taken from recent literature and new ones proposed in this study from real-world received signal power measurements in outdoor areas, including cities and suburbs. Using this data, we trained a random forest (RF) model for received signal power predictions. After training, we analyzed the importance of each feature to create a simpler ML model that maintains good prediction accuracy. Our results show that we can use only the 25 most important features to build a less complex model with a small error difference of 0.14 dB compared with the original model with 64 features.