ABSTRACT

Passive remote sensing services are indispensable in modern society. One important remote sensing application for Earth science and climate studies is soil moisture monitoring, which provides crucial information for agricultural management; forecasting severe weather, floods and droughts; and climate modeling and prediction. In parallel, modern society also depends heavily on active wireless communications technologies for commerce, transportation, health, science, and defense. Unfortunately, the growth of active wireless systems often increases radio frequency (RF) interference (RFI) experienced by passive systems. At best, RFI may reduce the accuracy of the passive system's measurements; at worst, it may render them useless. The goal of this project is to develop advanced signal processing, resource management and artificial intelligence (AI) techniques at the active and passive users to enable them to coexist in the same RF bands, thereby making more spectrum available to active systems while protecting the passive systems from RFI. The results will be presented to scientists, regulators, industry and standardization bodies that shape future wireless systems and spectrum access rules. The project will support the PIs' efforts to broaden the participation of students from underrepresented minority groups in engineering in collaboration with well-established programs at their institutions. Students trained through this project will be positioned to pioneer advanced wireless systems that are adaptable and can operate outside of dedicated RF spectrum. The testbed technology, methodology, and collected datasets will be shared with the scientific community and public through repositories and community research testbeds.

This project combines emerging technologies to address research challenges across multiple layers of the network protocol stack and across active and passive RF systems to tackle the critical problem of active-passive RF spectrum coexistence. It develops novel sparsity and AI-based RFI detection and mitigation techniques at the physical and application layers of passive sensing systems. It introduces a wireless channel virtualization and waveform optimization framework at the physical layer of active transceivers--applicable to current and next generation wireless systems--to enable AI-based sparse time-frequency scheduling at the active transmitter's physical and medium access control layers. The proposed algorithms and waveforms will be co-optimized with the passive sensing system's RFI detection and mitigation strategy using offline training to further improve spectrum coexistence. To this end, the project is designing and developing a one-of-a-kind testbed for collecting, processing and sharing remote sensing datasets in conjunction with ground and drone-based active communication systems with ground truth data.