Collaborative Research: CNS Core: Small: Beyond-5G Extreme Mobility: Issues and Solutions

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The current 4G/5G cannot ensure satisfactory reliability and performance for many emerging usage scenarios, such as vehicle-to-everything, high-speed rails, low earth orbit satellites, and drones. This project proposes a forward-looking solution suite for beyond-5G extreme mobility. It eliminates the 4G/5G limitations for extreme mobility by enabling predictive wireless design and simplified yet more efficient wide-area mobility support. If successful, this work will significantly advance the state-of-the-art in wireless networks. To maximize the impact, the researchers will collaborate with industry and release software to the public. The research outcome will also be incorporated into the graduate and undergraduate curriculum.This project proposes a forward-looking, transformative solution suite to beyond-5G extreme mobility. It first unveils 5G's deficiencies in extreme mobility under various scenarios and studies the client and infrastructure's proper roles in supporting them. It then develops novel approaches to accurately predict wireless channel by extracting delay-Doppler of the multipath and exploiting their temporal locality. In this way, it eliminates slow channel feedback in high-speed mobility and adapts to the fast-varying channel in extreme mobility. It further uses the predicted channel to enable predictive rate adaptation, resource allocation, and MIMO optimization under high mobility. Finally, it designs latency-friendly and interpretable distributed machine learning (ML) to help clients analyze the latency bottlenecks, and perform cross-layer latency optimizations. The proposed research will be evaluated using a software-defined radio prototype and large-scale emulation driven by operational 4G/5G traces.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.