PathFinder: Advancing Path Loss Prediction for Single-to-Multi-Transmitter Scenario

📰 ArXiv cs.AI

PathFinder advances path loss prediction for single-to-multi-transmitter scenarios in 5G networks and IoT applications

advanced Published 30 Mar 2026

Action Steps

Develop proactive environmental modeling to account for varying building densities and transmitter configurations
Implement deep learning-based methods that generalize well under distribution shifts
Evaluate model performance in realistic multi-transmitter scenarios

Who Needs to Know This

Telecom engineers and researchers on a team benefit from this work as it improves the accuracy of radio path loss prediction, while data scientists and AI engineers can apply these findings to develop more robust models

Key Insight

💡 Proactive environmental modeling and robust deep learning methods can enhance radio path loss prediction accuracy