161 citations · 487 across the 44 of their papers we have counts for
44 papers
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…
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Cen Liu, Guangxu Zhu, Fan Liu +2
The millimeter wave (mmWave) radar sensing-aided communications in vehicular mobile communication systems is investigated. To alleviate the beam training overhead under high mobili…
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…
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics
Boning Zhang, Dongzhu Liu, Osvaldo Simeone +1
The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that…
Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control
Yuding Liu, Dongzhu Liu, Guangxu Zhu +2
Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framew…
Wireless Image Transmission with Semantic and Security Awareness
Maojun Zhang, Yang Li, Zezhong Zhang +2
Semantic communication is an increasingly popular framework for wireless image transmission due to its high communication efficiency. With the aid of the joint-source-and-channel (…
Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G
Guangxu Zhu, Zhonghao Lyu, Xiang Jiao +5
Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelli…
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…
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
Xinrao Li, Tong Zhang, Shuai Wang +3
Edge intelligence autonomous driving (EIAD) offers computing resources in autonomous vehicles for training deep neural networks. However, wireless channels between the edge server…
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…
Over-the-Air Computation with Imperfect Channel State Information
Yilong Chen, Guangxu Zhu, Jie Xu
This paper investigates the effect of imperfect channel state information (CSI) on the over-the-air computation (AirComp) system, in which multiple wireless devices (WDs) send indi…
Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design
Xiaoyang Li, Fan Liu, Ziqin Zhou +4
To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further co…
Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication
Peixi Liu, Guangxu Zhu, Wei Jiang +3
This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communi…
Training Time Minimization for Federated Edge Learning with Optimized Gradient Quantization and Bandwidth Allocation
Peixi Liu, Jiamo Jiang, Guangxu Zhu +5
Training a machine learning model with federated edge learning (FEEL) is typically time-consuming due to the constrained computation power of edge devices and limited wireless reso…
Transmission Power Control for Over-the-Air Federated Averaging at Network Edge
Xiaowen Cao, Guangxu Zhu, Jie Xu +1
Over-the-air computation (AirComp) has emerged as a new analog power-domain non-orthogonal multiple access (NOMA) technique for low-latency model/gradient-updates aggregation in fe…
Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication
Zhonghao Lyu, Guangxu Zhu, Jie Xu
This paper studies the UAV-enabled integrated sensing and communication (ISAC), in which UAVs are dispatched as aerial dual-functional access points (APs) for efficient ISAC. In pa…
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Maojun Zhang, Guangxu Zhu, Shuai Wang +3
The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server.…
Data Partition and Rate Control for Learning and Energy Efficient Edge Intelligence
Xiaoyang Li, Shuai Wang, Guangxu Zhu +3
The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the w…
Accelerating Edge Intelligence via Integrated Sensing and Communication
Tong Zhang, Shuai Wang, Guoliang Li +3
Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, whic…
Symbiotic Sensing and Communications Towards 6G: Vision, Applications, and Technology Trends
Zhiqin Wang, Kaifeng Han, Jiamo Jiang +5
Driven by the vision of intelligent connection of everything and digital twin towards 6G, a myriad of new applications, such as immersive extended reality, autonomous driving, holo…
Optimized Power Control Design for Over-the-Air Federated Edge Learning
Xiaowen Cao, Guangxu Zhu, Jie Xu +2
This paper investigates the transmission power control in over-the-air federated edge learning (Air-FEEL) system. Different from conventional power control designs (e.g., to minimi…
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis
Zezhong Zhang, Guangxu Zhu, Rui Wang +2
Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component…
Optimized Power Control for Over-the-Air Federated Edge Learning
Xiaowen Cao, Guangxu Zhu, Jie Xu +1
Over-the-air federated edge learning (Air-FEEL) is a communication-efficient solution for privacy-preserving distributed learning over wireless networks. Air-FEEL allows "one-shot"…
Over-the-Air Computing for Wireless Data Aggregation in Massive IoT
Guangxu Zhu, Jie Xu, Kaibin Huang +1
Wireless data aggregation (WDA), referring to aggregating data distributed at devices (e.g., sensors and smartphone), is a common operation in 5G-and-beyond machine-type communicat…