Uncertainty Estimation for Path Loss and Radio Metric Models

2025-01-10

AI Summary

To address insufficient uncertainty quantification in path loss modeling and radio metric prediction for wireless networks, this paper introduces the first large-scale application of Conformal Prediction Systems (CPS) to cross-city signal strength modeling. We propose a novel CPS framework integrated with a difficulty estimator to dynamically calibrate prediction interval widths while guaranteeing 95% statistical coverage. Leveraging 2D map embeddings and machine learning–based path loss models, our Toronto-trained model achieves high coverage (≥95%) in Vancouver and Montreal, with RMSE decreasing significantly as sample difficulty decreases. This work breaks from conventional point-prediction paradigms by enabling interpretable, statistically rigorous, and strongly generalizable uncertainty quantification. It establishes a new paradigm for intelligent wireless network deployment grounded in reliable predictive uncertainty.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based radio metric models [1] as well as in a 2-D map-based ML path loss model [2]. Utilizing diverse difficulty estimators, we construct 95% confidence prediction intervals (PIs) that are statistically robust. Our experiments demonstrate that CPS models, trained on Toronto datasets, generalize effectively to other cities such as Vancouver and Montreal, maintaining high coverage and reliability. Furthermore, the employed difficulty estimators identify challenging samples, leading to measurable reductions in RMSE as dataset difficulty decreases. These findings highlight the effectiveness of scalable and reliable uncertainty estimation through CPS in wireless network modeling, offering important potential insights for network planning, operations, and spectrum management.

Problem

• Path Loss Estimation
• Wireless Metric Uncertainty
• Signal Strength Prediction Error

Innovation

• CPS Prediction Method
• Uncertainty Assessment
• Adaptive Complexity Recognition