15 citations · 48 across the 19 of their papers we have counts for
19 papers
Task-Oriented Multimodal Edge Intelligence via Integrated Sensing-Communication-Computation
Weiwei Chen, Yinghui He, Zhong Ye +2
Integrated sensing, communication, and computation (ISCC) has recently emerged as a unified framework for enabling edge intelligence. However, existing ISCC designs predominantly r…
Joint Sensing, Communication, and Computation for Vertical Federated Edge Learning in Edge Perception Network
Xiaowen Cao, Dingzhu Wen, Suzhi Bi +4
Combining wireless sensing and edge intelligence, edge perception networks enable intelligent data collection and processing at the network edge. However, traditional sample partit…
A Survey on Integrated Sensing, Communication, and Computation
Dingzhu Wen, Yong Zhou, Xiaoyang Li +3
The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision r…
Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy
Xiang Jiao, Dingzhu Wen, Guangxu Zhu +3
Edge-device co-inference, which concerns the cooperation between edge devices and an edge server for completing inference tasks over wireless networks, has been a promising techniq…
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…
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
Mingzhao Guo, Dongzhu Liu, Osvaldo Simeone +1
This paper presents a novel gradient compression method for federated learning (FL) in wireless systems. The proposed method centers on a low-rank matrix factorization strategy for…
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…
Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
Dingzhu Wen, Xiaoyang Li, Yong Zhou +3
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic commun…
Task-Oriented Communications for 6G: Vision, Principles, and Technologies
Yuanming Shi, Yong Zhou, Dingzhu Wen +3
Driven by the interplay among artificial intelligence, digital twin, and wireless networks, 6G is envisaged to go beyond data-centric services to provide intelligent and immersive…
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…
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
Dingzhu Wen, Peixi Liu, Guangxu Zhu +4
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) t…
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…
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices
Dingzhu Wen, Ki-Jun Jeon, Kaibin Huang
Federated learning (FL) is a popular framework for training an AI model using distributed mobile data in a wireless network. It features data parallelism by distributing the learni…
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…
Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks
Dingzhu Wen, Guangxu Zhu, Kaibin Huang
One basic operation of Internet-of-Things (IoT) networks is aggregating distributed sensing-data over wireless-channels for function-computation, called wireless-data-aggregation (…
Energy- and Spectral-Efficiency Tradeoff in Full-Duplex Communications
Dingzhu Wen, Guanding Yu, Rongpeng Liy +2
This paper investigates the tradeoff between energyefficiency (EE) and spectral-efficiency (SE) for full-duplex (FD) enabled cellular networks.We assume that small cell base statio…