Ruiyang Zhu

ORCID: 0000-0002-4524-5250
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About
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Research Areas
  • Vehicular Ad Hoc Networks (VANETs)
  • Advanced MIMO Systems Optimization
  • Green IT and Sustainability
  • Autonomous Vehicle Technology and Safety
  • Image and Video Quality Assessment
  • Nuclear Receptors and Signaling
  • Impact of Light on Environment and Health
  • Privacy-Preserving Technologies in Data
  • Traffic control and management
  • Wireless Networks and Protocols
  • Immune cells in cancer
  • Telecommunications and Broadcasting Technologies
  • Video Surveillance and Tracking Methods
  • Adversarial Robustness in Machine Learning

Wuhan University
2025

Zhongnan Hospital of Wuhan University
2025

University of Michigan
2021-2024

Michigan United
2024

Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study performance, power consumption, and application quality-of-experience (QoE) commercial 5G networks in wild. We examine different carriers, schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave sub 6-GHz), protocol configurations (_e.g._ Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (_i.e._ User Equipment), upper-layer applications...

10.1145/3452296.3472923 article EN 2021-08-09

With 5G's support for diverse radio bands and different deployment modes, e.g., standalone (SA) vs. non-standalone (NSA), mobility management - especially the handover process becomes far more complex. Measurement studies have shown that frequent handovers cause wild fluctuations in 5G throughput, worst, service outages. Through a cross-country (6,200 km+) driving trip, we conduct in-depth measurements to study current practices adopted by three major U.S. carriers. Using this rich dataset,...

10.1145/3544216.3544217 article EN 2022-08-11

Cooperative perception significantly enhances the performance of connected autonomous vehicles. Instead purely relying on local sensors with limited range, it enables multiple vehicles and roadside infrastructures to share sensor data perceive environment collaboratively. Through our study, we realize that cooperative systems is in real-world deployment due (1) out-of-sync during fusion (2) inaccurate localization occluded areas. To address these challenges, develop RAO, an innovative,...

10.1145/3570361.3613271 article EN Proceedings of the 28th Annual International Conference on Mobile Computing And Networking 2023-09-30

Neural-enhanced video streaming (e.g., super-resolution) is an ongoing revolution which can provide extremely high-quality services breaking the restriction of bandwidth. However, such enhancements require intense computation power that not affordable for a single mobile device, hinders their real-world deployment. To address limitation, we propose OASIS, first system facilitates multiple users in close proximity to execute neural-enhanced realtime. this end, OASIS intelligently distributes...

10.1145/3625468.3647610 article EN mit 2024-04-15

Collaborative Vehicular Perception (CVP) enables connected and autonomous vehicles (CAVs) to cooperatively extend their views through wirelessly sharing sensor data. Existing CVP systems employ either a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) view exchange paradigm. In this paper, we advocate hybrid design: our developed system, Harbor, employs V2I as its fundamental underlying framework, opportunistically V2V boost the performance. (helpers) may serve relays assist other...

10.1145/3666025.3699328 article EN 2024-11-04

5G and future 6G networks support diverse combinations of access technologies, architectures, radio frequencies, with each combination termed as a "band" henceforth. Through comprehensive measurements in 12 cities across 5 countries, we experimentally show that operator-configured default bands are often highly sub-optimal, particularly under mobility. We then propose smart band switching, where UE's can be dynamically changed to improve the network performance boost application QoE. discuss...

10.1145/3638550.3641132 article EN 2024-02-20

Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth potential security risks. CAVs' driving decisions rely on remote untrusted data, making them susceptible to attacks carried out malicious participants in collaborative perception system. However, analysis countermeasures for such threats are absent. To understand impact vulnerability, we break ground proposing...

10.48550/arxiv.2309.12955 preprint EN cc-by arXiv (Cornell University) 2023-01-01
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