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, Prof. Hyeji Kim, Prof. Songwu Lu

·        Ghufran Baig

·        Changhan Ge

·        Qianru Li

·        Zhehui Zhang

 

Collaborator

·        Yuanjie Li

Publication

Abstract: The wireless signal propagates via multipath arising from different reflections and penetration between a transmitter and receiver. Ob- taining multipath profiles (e.g., delay and Doppler along each path) from received wireless signals enables many important applica- tions, such as channel prediction and crossband channel estimation (i.e., estimating the channel on a different frequency). The benefit of multipath estimation further increases with mobility since the channel in that case is less stable and more important to track. Yet high-speed mobility poses significant challenges to multipath estimation. In this paper, instead of using time-frequency domain channel representation, we leverage the delay-Doppler domain rep- resentation to accurately extract and predict multipath properties. Specifically, we use impulses in the delay-Doppler domain as pilots to estimate the multipath parameters and apply the multipath infor- mation to predicting wireless channels as an example application. Our design rationale is that mobility is more predictable than the wireless channel since mobility has inertial while the wireless chan- nel is the outcome of a complicated interaction between mobility, multipath, and noise. We evaluate our approach via both acoustic and RF experiments, including vehicular experiments using USRP. Our results show that the estimated multipath matches the ground truth, and the resulting channel prediction is more accurate than the traditional channel prediction schemes.

  • Movement-based Reliable Mobility Management for Beyond 5G Cellular Networks. Zhehui Zhang, Yuanjie Li, Qianru Li, Jinghao Zhao, Ghufran Baig, Lili Qiu, Songwu Lu. IEEE/ACM Transactions on Networking. 2022.

    • Abstract:  Extreme mobility becomes a norm rather than an exception with emergent high-speed rails, drones, industrial IoT, and many more. 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, decsion, and execution. We devise REM, Reliable Extreme Mobility management for beyond 5G cellular networks while maintaining backward compatibility to 4G/5G. REM shifts to movement-based mobility management in the delay-Doppler domain. Its signaling overlay relaxes feedback via cross-band 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. REM reduces the network failures by up to an order of magnitude, eliminates policy conflicts, and improves application performance by 31.8% - 88.3% compared to legacy 4G/5G.

  • Beyond 5G: Reliable Extreme Mobility Management. Yuanjie Li, Qianru Li, Zhehui Zhang, Ghufran Baig, Lili Qiu, Songwu Lu. In Proc. of ACM SIGCOMM 2020.

    • 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.