NSF Funded SWIFT-SAT Project Website

Award: 2332760; PI: Z. Ding (UC Davis)

Visit PI Institutional Research Pages for more related works: Prof. Z. Ding

SWIFT-SAT: Network Adaptation Based on Physics-Inspired Learning Framework for Radio Coexistence of Terrestrial and Satellite Information Systems

Project Overview

Major advances in information technologies have stimulated numerous novel concepts and wireless innovations including 3GPP's 5G terrestrial and non-terrestrial network (NTN) services. As high volumes of data traffic from a rich plethora of applications ranging from artificial intelligence, automation, IoT, and virtual/augmented reality continue to cram into existing system of limited capacity, the ever-increasing spectrum need motivates the expansion of wireless networking into new radio bands. As the terrestrial radio spectrum expands, spectrum coexistence between terrestrial and satellite systems becomes increasingly critical. This collaborative SWIFT-SAT project aims to develop physics-inspired learning frameworks by integrating physical radio propagation models and data-driven learning machines to facilitate efficient and harmonious coexistence between terrestrial and satellite systems. Leveraging prior knowledge from measurement data and the constraints imposed by radio physical models, the proposed research activities are geared towards establishing accurate radio interference models and databases to empower advanced network optimization and adaptation. Accurate radio coverage map (radio-map) estimation is critical to interference-limited coexistence between terrestrial and satellite systems. Major project thrusts are directed at dynamic estimation and prediction of radio-map as well as their integration into wireless network optimization and adaptation under coexistence constraints. Requiring only sparse observations in spatial, frequency, and temporal domains, the investigators employ physics-inspired and model-driven learning approaches to accurately estimate co-channel and adjacent channel interferences based on radio-map estimation of coexisting wireless systems. The project innovation further includes learning-based approaches guided by propagation models for intelligent wireless network optimization and interference diagnosis facilitated by radio-map information. Through the proposed innovation, model-based radio coverage estimation can anticipate interference anomalies, can detect or diagnose network outages quickly, and can respond with effective network adaptation policies. The proposed solutions and project outcomes are expected to significantly impact future technology development for interference-limited coexistence between terrestrial and satellite systems. More broadly, this work promotes reliable and intelligent wireless systems across data acquisition, communication, sensing, and distributed computing. Broader impacts from this project include many new educational opportunities stimulated by this research. Already in partnership with local inner-city schools, the project team plans to incorporate into this project learning opportunities in STEM for K-12 and under-represented talents.

UC Davis Participants

PI: Z. Ding;

Researcher Team Members: Yibo Ma, Dr, Feng Ouyang (JHU-APL)

UC Davis Publications

1. Y. Ma, Y. Zhou, S. Zhang and Z. Ding, ``Dual-GRE: Dual-Phase Enhancement in Radiomap Estimation Based on Graph Attention,'' in IEEE Wireless Communications Letters, 2025. doi: 10.1109/LWC.2025.3578024.


2. Wijesinghe, S. Zhang, S. Wanninayaka, W. Wang and Z. Ding, ``Diff-GO+: An Efficient Diffusion Goal-Oriented Communication System with Local Feedback,'' in IEEE Transactions on Wireless Communications, 2025. doi: 10.1109/TWC.2025.3554442


3. Y. Zhou, A. Wijesinghe, Y. Ma, S. Zhang and Z. Ding, ``TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation,'' in IEEE Wireless Communications Letters, vol. 14, no. 5, pp. 1401-1405, May 2025, doi: 10.1109/LWC.2025.3543513.


4. Y. -C. Lin, Y. Xin, T. -S. Lee, J. C. Zhang and Z. Ding, ``Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback,'' in IEEE Transactions on Wireless Communications, vol. 24, no. 2, pp. 1117-1131, Feb. 2025, doi: 10.1109/TWC.2024.3505239


5. Zhang, Songyang, Brian Choi, Feng Ouyang, and Zhi Ding, ``Physics-inspired machine learning for radiomap estimation: Integration of radio propagation models and artificial intelligence. IEEE Communications Magazine, 62, no. 8, pp. 155-161, 2024. doi: 10.1109/MCOM.001.2300782.


6. A. Wijesinghe, S. Zhang and Z. Ding, ``PS-FedGAN: An Efficient Federated Learning Framework With Strong Data Privacy,'' in IEEE Internet of Things Journal, vol. 11, no. 16, pp. 27584-27596, 15 Aug.15, 2024, doi: 10.1109/JIOT.2024.3399226.



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UC Davis: Prof. Z. Ding