SHAPRP: A SHAP-Guided Framework for Efficient RSS Estimation in 5G/B5G Networks

Abstract

As bandwidth-intensive applications proliferate and the usage of wireless devices surges, fifth-generation (5G) and beyond (B5G) networks are challenged to enhance coverage, reduce latency, and improve efficiency. The application of machine learning (ML) models for received signal strength (RSS) estimation is a powerful tool. This study evaluates various ML models—categorical boosting (CatBoost), extreme randomized trees (ETs), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost)—for effective estimation of RSS. Additionally, we apply explainable artificial intelligence (XAI) methodologies, especially the Shapley additive explanation (SHAP) framework. Our investigation reveals the sophisticated mechanisms within these models, notably highlighting the exceptional accuracy of the ET model. We further introduce SHAPRP, in which SHAP attributions reduce the input space and sparse regression selects a compact subset of the ET ensemble. The results are obtained from a single-operator rural/semi-rural campaign and constitute a case study, where the trained estimator is deployment-specific and is not a pre-trained model applicable to arbitrary 5G/B5G scenarios, so what transfers is SHAPRP itself.