RUI: SpecEES: Collaborative Research: Enabling Secure, Energy-Efficient, and Smart In-Band Full Duplex Wireless

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In-band full-duplex (IBFD) wireless communication technique has tremendous potentials in spectral efficiency because of its simultaneous transmission and reception of information. Although IBFD wireless communication technique has been theoretically investigated and analyzed for years, it remains very challenging to be systemically enabled in practice because of a few hurdles. This project will design and develop deep learning resolutions of self-interference cancellation, power control, and security for future IBFD wireless communication systems. The research can potentially double the wireless spectrum efficiency and impact future wireless standards and policies. Outcomes as publications and open source codes will be made available to the research community to significantly facilitate the research on deep learning-based wireless communications. This project will integrate the research outcomes into course curricula to promote training workforce with knowledge and skills in deep learning and future wireless system design. Underrepresented students will be recruited to participate as research assistants or through special programs, e.g., the Louis Stokes Alliance for Minority Participation Program or the Sloan Engineering Program at the collaborative institutions. This research tackles three major challenges and problems to enable secure, spectrum-efficient, and energy-efficient IBFD wireless communication systems. First, this project will design deep learning based all-digital self-interference cancellation solutions with the potential of doubling the spectrum efficiency. Such design with nonlinear solutions is expected to model the self-interference much more accurately than conventional solutions. The proposed per-symbol estimation of wireless channel condition will provide the highest resolution of channel dynamics to upper layers for cross-layer designs. Second, deep learning power control solutions will be designed to maximize the energy efficiency of IBFD wireless system. These solutions are expected to achieve optimal performance while overcoming the computational and mathematical hurdles in traditional solutions. Third, by data-mining the IBFD channel dynamics, new solutions for wireless security with high degrees of efficiency and secrecy will be developed for IBFD wireless communication systems and networks.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.