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

arXiv: https://arxiv.org/abs/2512.04100 · HTML: https://mlsanity.com/paper/2512.04100/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning](https://mlsanity.com/paper/2410.13228/) (2024)
- [Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography](https://mlsanity.com/paper/2202.03269/) (2022)
- [Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning](https://mlsanity.com/paper/1804.07824/) (2018)
- [A Scalable and Generalizable Pathloss Map Prediction](https://mlsanity.com/paper/2312.03950/) (2023)
- [Integrated Interpolation and Block-term Tensor Decomposition for Spectrum Map Construction](https://mlsanity.com/paper/2402.17138/) (2024)
- [Location-free Spectrum Cartography](https://mlsanity.com/paper/1812.11539/) (2018)
- [Quantized Radio Map Estimation Using Tensor and Deep Generative Models](https://mlsanity.com/paper/2303.01770/) (2023)
- [ReVeal: A Physics-Informed Neural Network for High-Fidelity Radio Environment Mapping](https://mlsanity.com/paper/2502.19646/) (2025)
- [Design and Implementation of ARA Wireless Living Lab for Rural Broadband and Applications](https://mlsanity.com/paper/2408.00913/) (2024)
- [Physics-Informed Generative Approaches for Wireless Channel Modeling](https://mlsanity.com/paper/2503.05988/) (2025)
- [ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning](https://mlsanity.com/paper/2308.10193/) (2023)
- [Wireless Spectrum in Rural Farmlands: Status, Challenges and Opportunities](https://mlsanity.com/paper/2407.04561/) (2024)

## Abstract (arXiv)

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 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.