Overview

This is a collaborative project with University of Texas, Austin. Despite the global deployment, current 4G/5G networked systems still cannot guarantee reliability and performance for extreme mobility scenarios (vehicle-to-everything, high-speed rails, low earth orbit satellites, drones, factory automation) and mmWave. In this project, we explore a forward-looking, beyond-5G enhancement of reliability and performance for extreme mobility. We enable predictive wireless and efficient wide-area mobility management under extreme mobility. Our approach consists of three components: (1) We develop novel channel prediction algorithms that use impulses in the delay-Doppler domain as pilots to estimate the delay-Doppler and predict the channel; (2) We enable predictive wireless performance by applying our channel prediction to dynamically adapt data rate, optimize MIMO, and allocate resources; and (3) To enable efficient and conflict-free wide-area mobility, we stabilize the triggering conditions with our predictive wireless, simplify the mobility decisions for conflict-free policies and low management complexity, and smooth the mobility actions with cross-layer, distributed learning-driven approaches among clients.

Publications

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

Software Release

An open and application-driven simulator will be released to demonstrate predictive wireless and reliable mobility management under extreme mobility. Web link

Demos

We will release a complete demo in which the mobility management simulator is applied to mobile AR apps.

Volumetric Video High-speed Truck on Intelligent Freeway

Team Members

• Qianru Li  (University of California, Los Angeles)
• Zhehui Zhang  (University of California, Los Angeles)
• Jinghao Zhao  (University of California, Los Angeles)
• Ghufran Baig  (The University of Texas at Austin)
• Lili Qiu  (The University of Texas at Austin)
• Songwu Lu  (University of California, Los Angeles)

Research Support

We gratefully acknowledge research support from NSF (CNS-2008026).