Collaborative research: nets: physics-enhanced AI for spectrum awareness and radiomap-assisted network management

The National Science Foundation Division of Information and Intelligent Systems awarded the University of Louisiana at Lafayette $200,000 on October 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop physics-enhanced artificial intelligence methods for radio map estimation and wireless network management.

The project, running through September 30, 2029, integrates wireless propagation principles with machine learning to construct accurate radio maps from sparse measurements and support network planning, infrastructure deployment, system optimization, and network fault diagnosis. Research directions include developing physics-enhanced learning methods that incorporate domain knowledge into feature extraction and neural network architectures; incorporating radio maps into wireless network management as inputs and environmental descriptors; and designing advanced learning methods for joint radio map estimation and network management. The work will advance algorithms, models, evaluation methods, datasets, and software tools for managing future wireless communication systems. The project also supports student training and curriculum development.

Work is performed in Lafayette, Louisiana.

Abstract

Next-generation wireless networks will support rapidly growing numbers of connected devices and users that share wireless spectrum in complex and dynamic environments. However, accurately characterizing radio environments across various spatial locations, terrain conditions, and network activities remains challenging. This project develops new machine learning methods for estimating and utilizing radio maps to support network planning, infrastructure deployment, system optimization, and network fault diagnosis. The project promotes more effective use of scarce spectrum resources, improves wireless connectivity for emerging applications, and prepares the future workforce through student training, curriculum development, and dissemination of research outcomes. This project develops a physics-enhanced artificial intelligence (AI) framework for radio map estimation and radio map-assisted wireless network management. The research integrates wireless propagation principles with advanced machine learning techniques to construct accurate radio maps from sparse measurements. The project investigates three research directions. First, physics-enhanced learning methods are developed for radio map estimation by incorporating domain knowledge into feature extraction, learning objectives, and neural network architectures. Second, radio maps are incorporated into wireless network management as inputs, performance metrics, and environmental descriptors to support network planning, optimization and diagnosis. Third, advanced learning methods are designed for joint radio map estimation and network management. The project advances both wireless networking and AI technologies through the development of new algorithms, models, evaluation methods, datasets, and software tools for effective and reliable management of future wireless communication systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.