ReVeal-MT: A Physics-Informed Neural Network for Multi-Transmitter Radio Environment Mapping

2025-11-22

AI Summary

Radio environment map (REM) construction suffers from low accuracy under multi-transmitter coexistence, primarily due to the coupled effects of shadowing and adjacent-channel interference, which invalidate conventional propagation models. Method: We propose ReVeal-MT—a physics-informed neural network (PINN) that uniquely incorporates multi-source partial differential equation (PDE) residuals into its loss function, enabling joint, physics-consistent reconstruction of received signal strength indicator (RSSI) fields from sparse measurements. The model integrates PDE-based multi-transmitter propagation modeling with real-world sparse RF sensing data from an experimental testbed. Results: Evaluated over a 370 km² area using only 45 sampling points, ReVeal-MT achieves a root-mean-square error (RMSE) of 2.66 dB—significantly outperforming both 3GPP/ITU-R standard models and single-source PINN baselines—while maintaining computational efficiency, thereby supporting dynamic spectrum sharing deployments with primary user protection.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling Secondary Users~(SUs) to access underutilized spectrum bands while protecting Primary Users~(PUs). While existing models have made progress, they often degrade in performance when multiple transmitters coexist, due to the compounded effects of shadowing, interference from adjacent transmitters. To address this challenge, we extend our prior work on Physics-Informed Neural Networks~(PINNs) for single-transmitter mapping to derive a new multi-transmitter Partial Differential Equation~(PDE) formulation of the Received Signal Strength Indicator~(RSSI). We then propose emph{ReVeal-MT} (Re-constructor and Visualizer of Spectrum Landscape for Multiple Transmitters), a novel PINN which integrates the multi-source PDE residual into a neural network loss function, enabling accurate spectrum landscape reconstruction from sparse RF sensor measurements. ReVeal-MT is validated using real-world measurements from the ARA wireless living lab across rural and suburban environments, and benchmarked against 3GPP and ITU-R channel models and a baseline PINN model for a single transmitter use-case. Results show that ReVeal-MT achieves substantial accuracy gains in multi-transmitter scenarios, e.g., achieving an RMSE of only 2.66,dB with as few as 45 samples over a 370-square-kilometer region, while maintaining low computational complexity. These findings demonstrate that ReVeal-MT significantly advances radio environment mapping under realistic multi-transmitter conditions, with strong potential for enabling fine-grained spectrum management and precise coexistence between PUs and SUs.

Problem

• Mapping radio environment with multiple transmitters accurately
• Addressing performance degradation from shadowing and interference effects
• Reconstructing spectrum landscape from sparse RF measurements

Innovation

• Physics-informed neural network for multi-transmitter radio mapping
• Integrates multi-source PDE residual into loss function
• Accurate reconstruction from sparse RF sensor measurements