CIF: Small: Deep Generative Models for Ultra High-Dimensional Communication

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Future wireless communications applications like untethered virtual reality, connected vehicles, and high resolution mobile video content delivery will demand very high bit rates and reduced energy consumption. To fulfill these requirements, future wireless systems will use very high carrier frequencies enabled by deploying massive arrays of very small antennas. Such architectures are called ultra high dimensional (UHD) communication systems. This is a very challenging scenario for wireless communication, since the wireless channel is difficult to learn and the wireless energy must be radiated in a highly directional manner towards the intended receiver. Existing approaches to these problems will not scale to the UHD scenario in terms of complexity, power consumption, or overhead signaling. This exploratory project will address these challenges emerging in modern wireless communication systems. The specific focus of this project is on developing novel unsupervised learning techniques to handle these emerging challenges. In particular, this project will enable accurate and efficient channel estimation techniques, design novel data-driven beamforming schemes, and model the nonlinearities created by quantization. This project will further help in enabling power and bandwidth-efficient UHD communication systems, as well as establishing a new methodology and initializing communication over ultra high carrier frequencies (e.g., above 100 GHz).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.