Machine-Learning-Based Classification of Radio Frequency Building Loss

TL;DR

This paper introduces a machine learning framework that classifies RF building loss using crowdsourced data and public info, improving accuracy and confidence over traditional methods for better indoor wireless network planning.

Contribution

It presents a novel combination of supervised and semi-supervised learning on crowdsourced data for RF loss classification, outperforming traditional models.

Findings

Semi-supervised learning improves prediction accuracy and confidence.

SSL XGBoost achieves the most confident O2I loss classification.

SSL LightGBM yields the best performance for I2I loss.

Abstract

Accurate modeling of outdoor-to-indoor (O2I) and indoor-to-indoor (I2I) signal loss is important for improving indoor wireless network performance in dense urban areas. Traditional on-site measurements are expensive, time-consuming, and difficult to conduct across wide regions. Real-world datasets also tend to be noisy and imbalanced, which makes signal loss prediction challenging. This study presents a machine learning framework for classifying radio frequency (RF) building loss. The framework combines passively collected, crowdsourced user equipment (UE) data from 3GPP-compliant networks with public building information. We evaluated Random Forest, XGBoost, LightGBM, and a voting classifier using both supervised (SL) and semi-supervised learning (SSL). Compared to SL-only inference, the proposed SL and SSL framework improved both prediction accuracy and confidence under identical data constraints, achieving up to 12.6% relative accuracy gain for O2I loss and 3.4% for I2I loss, while reducing prediction entropy by up to 8.4%. Among the evaluated models, SSL XGBoost provided the most confident O2I loss classification, whereas SSL LightGBM achieved the best performance for I2I loss. These results demonstrate that the proposed approach provides a practical, data-driven alternative to traditional models, with promising potential to support better network planning and indoor coverage optimization.