4 citations · 15 across the 9 of their papers we have counts for
14 papers
Collaborative Edge AI Inference over Cloud-RAN
Pengfei Zhang, Dingzhu Wen, Guangxu Zhu +3
In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture rea…
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Zhonghao Lyu, Yuchen Li, Guangxu Zhu +3
In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fi…
Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN
Zezhong Zhang, Guangxu Zhu, Junting Chen +1
In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the d…
Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design
Ziqin Zhou, Xiaoyang Li, Guangxu Zhu +3
In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integr…
Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference
Zeming Zhuang, Dingzhu Wen, Yuanming Shi +3
Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing amb…
Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI
Hong Xing, Guangxu Zhu, Dongzhu Liu +3
With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc. featuring massive data sensing, analyzing and inference as well critical la…