96 citations · 211 across the 28 of their papers we have counts for
13 papers · 1 filter
Joint Computing, Pushing, and Caching Optimization for Mobile Edge Computing Networks via Soft Actor-Critic Learning
Xiangyu Gao, Yaping Sun, Hao Chen +2
Mobile edge computing (MEC) networks bring computing and storage capabilities closer to edge devices, which reduces latency and improves network performance. However, to further re…
Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning
Yao Tang, Guangxu Zhu, Wei Xu +3
Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted e…
Zero-shot Multi-level Feature Transmission Policy Powered by Semantic Knowledge Base
Yaping Sun, Hao Chen, Xiaodong Xu +2
Remote zero-shot object recognition, i.e., offloading zero-shot object recognition task from one mobile device to remote mobile edge computing (MEC) server or another mobile device…
Soft Actor-Critic Learning-Based Joint Computing, Pushing, and Caching Framework in MEC Networks
Xiangyu Gao, Yaping Sun, Hao Chen +2
To support future 6G mobile applications, the mobile edge computing (MEC) network needs to be jointly optimized for computing, pushing, and caching to reduce transmission load and…
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…
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach
Yining Wang, Mingzhe Chen, Tao Luo +4
In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textua…
An Overview on Over-the-Air Federated Edge Learning
Xiaowen Cao, Zhonghao Lyu, Guangxu Zhu +3
Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future beyond 5G (B5G) and 6G networks. In Air-F…
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning
Zezhong Zhang, Guangxu Zhu, Shuguang Cui
In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency. The…
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…
Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration
Peixi Liu, Guangxu Zhu, Shuai Wang +4
In this paper, we address the problem of joint sensing, computation, and communication (SC) resource allocation for federated edge learning (FEEL) via a concrete case study o…
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…
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…
Amplify-and-Forward Relaying for Hierarchical Over-the-Air Computation
Feng Wang, Jie Xu, Vincent K. N. Lau +1
This paper studies a hierarchical over-the-air computation (AirComp) network over a large area, in which multiple relays are exploited to facilitate data aggregation from massive W…