SWIFT:SMALL: Dynamic Wireless Resource Management and Transceiver Adaptation for Efficient Spectrum Utilization and Coexistence

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Advanced wireless networks and technologies are taking the center stage in the era of cyber-based data analytics and artificial intelligence (AI). Faced with the surging needs for high speed wireless data connections driven by widespread AI applications, effective solutions for effective spectrum utilization and coexistence in broadband wireless networks become increasingly critical. This project aims to develop new estimation methods and resource management tools for wide-area radio networks to efficiently and accurately assess the radio channel conditions and coverage quality map (radiomap) for providing high quality services to the huge number of smartphones and other wireless devices. The outcomes of this project can contribute significantly to the deployment of high speed wireless services and their broadening applications in networked AI applications. The broader impact from this research will also come through many educational opportunities by providing opportunities in STEM to K-12, women, and under-represented minority students.In wireless networks, accurate forward link RF channel estimation is critical to achieving high speed data services to mobile terminals. At the same time, radiomap of RF signal effect in a complex coverage environment provides vital information to coordinate network service stations to efficient utilize limited RF resources and to achieve effective interference mitigation in protected regions. This project aims to develop new practical algorithms and resource management tools for wide-area radio networks to efficiently and accurately assess the radio channel conditions and interference radiomap, to facilitate coexistence with protected RF applications serving potentially passive users. Specifically, the research group will leverage recent successes of machine learning and data analytics across a wide range of engineering and scientific applications to develop innovative learning-based algorithms for accurate RF channel estimation by minimizing the consumption of valuable resources while improving wireless quality and spectrum utilization. To simultaneously protect sensitive nodes of coexisting wireless applications, the project utilizes low cost and distributed sensor measurements to derive accurate radiomap estimation. Furthermore, the research group will extensively investigate the impact of spectrum allocation and dynamic link-adaptation on interference mitigation to protect sensitive services in designated areas.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.