15 citations · 25 across the 4 of their papers we have counts for
8 papers
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…
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…
Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
Dingzhu Wen, Ki-Jun Jeon, Mehdi Bennis +1
In this paper, we consider partitioned edge learning (PARTEL), which implements parameter-server training, a well known distributed learning method, in a wireless network. Thereby,…
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
Jinke Ren, Yinghui He, Dingzhu Wen +3
In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without e…
Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning
Dingzhu Wen, Mehdi Bennis, Kaibin Huang
To leverage data and computation capabilities of mobile devices, machine learning algorithms are deployed at the network edge for training artificial intelligence (AI) models, resu…
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
Dingzhu Wen, Xiaoyang Li, Qunsong Zeng +2
The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualizat…