22 citations · 48 across the 14 of their papers we have counts for
9 papers · 1 filter
Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting
Hai Wu, Qunsong Zeng, Kaibin Huang
For the 6G mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptive artificial intelligence on edge devices. However, the simult…
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…
Over-the-Air Multi-View Pooling for Distributed Sensing
Zhiyan Liu, Qiao Lan, Anders E. Kalør +2
Sensing is envisioned as a key network function of the 6G mobile networks. Artificial intelligence (AI)-empowered sensing fuses features of multiple sensing views from devices dist…
Semantic Data Sourcing for 6G Edge Intelligence
Kaibin Huang, Qiao Lan, Zhiyan Liu +1
As a new function of 6G networks, edge intelligence refers to the ubiquitous deployment of machine learning and artificial intelligence (AI) algorithms at the network edge to empow…
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
Dingzhu Wen, Xiang Jiao, Peixi Liu +3
Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient executio…
A Two-Timescale Approach to Mobility Management for Multi-Cell Mobile Edge Computing
Zezu Liang, Yuan Liu, Tat-Ming Lok +1
Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge serv…
Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting (Extended Version)
Zhiyan Liu, Qiao Lan, Kaibin Huang
The deployment of inference services at the network edge, called edge inference, offloads computation-intensive inference tasks from mobile devices to edge servers, thereby enhanci…
Accelerating Federated Edge Learning via Topology Optimization
Shanfeng Huang, Zezhong Zhang, Shuai Wang +2
Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the exi…
What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence
Qiao Lan, Dingzhu Wen, Zezhong Zhang +4
In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main t…