CIF: Small: NSF-DST: Zak-OTFS - How to Make Communication and Radar Sensing More Predictable in 6G

Over the last thirty years wireless applications have evolved from voice communication to high-speed data to integrated sensing and communications, and propagation environments have evolved to include both non-terrestrial and terrestrial networks. The standard approach to wireless signal processing is based on mathematical models, but as applications and environments evolve, Doppler spreads start to be measured in kilohertz, and the standard approach starts to break down. On the other hand, over the last ten years, machine learning has revolutionized image and natural language processing, and over the next ten years, it is expected to transform wireless signal processing. Machine learning algorithms are most effective when the underlying data stream is predictable, and this project is focused on engineering the wireless input-output relation so that it is predictable, and so that it only changes slowly, that is, at the same speed as the physics of the wireless environment. This project will develop the fundamental research necessary to enable wireless signal processing in the delay-Doppler domain. The research program is organized into three interconnected research thrusts: 1) design of modulation schemes that minimize the complexity of predicting the wireless input-output relation; 2) design of filters in the discrete delay-Doppler domain that enable integrated sensing and communication; and 3) design of new waveforms that optimize sensing performance. Expected outcomes include the possibility of model-free operation, which can enable communication when traditional model-dependent modes requiring mathematical models are out of reach. International collaboration between India and the US will broaden the project impact, and software and hardware testbeds will translate theory into practice. 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.