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

TL;DR

This work addresses the challenge of accurately mapping radio environments in multi-transmitter settings under limited transmitter information. It introduces ReVeal-MT, a physics-informed neural network that embeds a novel multi-transmitter PDE into the learning objective to reconstruct high-fidelity REMs from sparse RF-sensor data. The approach demonstrates substantial accuracy gains (RMSE ≈ 2.66 dB) over large geographic areas with few samples, outpacing stochastic, deterministic, and single-transmitter PINN baselines. The method’s data efficiency and physics-grounded design enable robust spectrum management and coexistence between primary and secondary users in complex rural and suburban deployments, with potential extensions to temporal dynamics and dynamic spectrum sharing.

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.

Figures (15)

• Figure 1: Visualization of signal source placement relative to the domain/geographic-region of interest. The signal source (denoted as X) influences the candidate sample locations (denoted as O) differently depending on its position, altering the source coverage boundary (denoted in green color).
• Figure 2: PINN architecture of ReVeal
• Figure 3: ARA Deployment
• Figure 4: Sampling Locations and Corresponding Terrain Conditions
• Figure 5: Variation in Channel Conditions across Rural and Suburban Terrains