NSF Project CNS #2008824

Beyond-5G Extreme Mobility: Issues and Solutions

Synopsis

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, 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. 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. If successful, this work will significantly advance the state-of-the-art in wireless networks.

Personnel

        Prof. Lili Qiu and Prof. Songwu Lu

        Ghufran Baig

        Changhan Ge

        Qianru Li

        Zhehui Zhang

 

Collaborator

        Yuanjie Li

Publication

Abstract: Extreme mobility has become a norm rather than an exception. However, 4G/5G mobility management is not always reliable in extreme mobility, with non-negligible failures and policy conflicts. The root cause is that, existing mobility management is primarily based on wireless signal strength. While reasonable in static and low mobility, it is vulnerable to dramatic wireless dynamics from extreme mobility in triggering, decision, and execution. We devise REM, Reliable Extreme Mobility management for 4G, 5G, and beyond. REM shifts to movement-based mobility management in the delay Doppler domain. Its signaling overlay relaxes feedback via crossband estimation, simplifies policies with provable conflict freedom, and stabilizes signaling via scheduling-based OTFS modulation. Our evaluation with operational high-speed rail datasets shows that, REM reduces failures comparable to static and low mobility, with low signaling and latency cost.

Broader Impacts

This project has potential to lay the technical foundations for beyond-5G systems (e.g., 6G and Wi-Fi 6/7). The anticipated outcome will likely inform the design for next-generation wireless and mobile networking, influence the beyond-5G standardization, and advance numerous mobile/IoT applications.

This project has provided invaluable opportunity for UT and UCLA students, and facilitated in creating a research pipeline between the two institutions. The research results have been incorporated in the graduate courses. The results will be disseminated through publications and open-source code release. We also plan to introduce research on 5G and beyond to K-12 students to encourage more students to participate in computer science and STEM education.