Author: Mukaram Shahid
Prof. Hongwei Zhang and his team at Iowa State University (ISU) have made a leap in spectrum cartography with the development of ReVeal (Reconstructor and Visualizer of Spectrum Landscape), a Physics-Informed Neural Network (PINN) that enables high-accuracy Radio Environment Mapping (REM) using sparse data. This breakthrough, enabled in part by real-world data from the ARA rural wireless living lab, was recently reported at 2025 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).
Accurate REM is critical for dynamic spectrum sharing, especially in environments where traditional methods fall short due to shadowing and lack of precise information about transmitters (e.g., location and transmission power) and the environment. For instance, traditional statistical channel models do not reflect the in-situ, specific terrain and environmental conditions; ray tracing tends to be computationally expensive and requires precise characterization of the complete propagation environment (e.g., vegetation, trees, buildings, materials), which tends to be difficult in practice. Spatial modeling techniques such as Kriging requires stationarity or dense sampling for optimal results, and existing neural networks tend to lack interpretability and do not leverage domain knowledge.
The ISU team has introduced ReVeal, a novel PINN framework that embeds a physics-based partial-differential-equation spatial model of signal power into a neural network loss function. This approach allows ReVeal to:
- Accurately model wireless signal propagation with sparse data (e.g., only requiring 30 spatial sample points over an area of ~514 km² in the ARA wireless living lab),
- Function without requiring detailed transmitter information (e.g., location, transmission power), and
- Significantly outperform existing methods such as the statistical models used in 3GPP and ITU-R specifications, ray tracing, and neural networks without domain knowledge as shown in the figure below; the RMSE of signal power estimation is as low as 1.95 dB in ReVeal.
The ARA Wireless Living Lab played a central role in the above study, and the dataset will be made available to the community soon. Researchers used ARA’s expansive, first-of-its-kind rural deployment in Iowa to collect real-world RSSI measurements over diverse terrains. The sparsity and heterogeneity of these measurements mimicked the real-world challenges spectrum regulators and dynamic users face in rural settings, making ARA the perfect proving ground for ReVeal.
ReVeal’s performance highlights its potential to be a critical enabler for dynamic spectrum sharing, especially in underserved and rural regions. Its ability to reconstruct high-fidelity signal maps from minimal data makes it attractive for:
- TV White Space (TVWS) applications,
- Cognitive radio systems,
- Efficient interference management, and
- Potentially contributing to national spectrum policy and planning.
The research team is now exploring the extension of ReVeal to dynamic environments with optimal spatiotemporal sampling and applying the approach to interference prediction, primary- and secondary-user coexistence management, and 5G/6G use cases.
Author’s background:
Mukaram Shahid is pursuing his Ph.D. from Iowa State University. He obtained his bachelor’s in electrical engineering from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan, and his master’s in computer engineering from Iowa State University. His research focuses on topics covering Dynamic Spectrum Sharing, Spectrum Policy Enforcement, and AI & Machine Learning applications in wireless communications.
