Collaborative Research: NeTS: Physics-Enhanced AI for Spectrum Awareness and Radiomap-Assisted Network Management

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.