96 citations · 211 across the 28 of their papers we have counts for
29 papers
Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs
Xiaowen Cao, Zhonghao Lyu, Shicheng Chu +6
The increasing scale and computational demands of large artificial intelligence models (LAIMs) present significant challenges for efficient inference in resource-constrained distri…
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…
ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration
Duo Wu, Dayou Zhang, Miao Zhang +3
The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to res…
Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
Hongyang Du, Ruichen Zhang, Yinqiu Liu +10
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy a…
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…
Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding
Zhonghao Lyu, Guangxu Zhu, Jie Xu +2
With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart fr…
Crossword: A Semantic Approach to Data Compression via Masking
Mingxiao Li, Rui Jin, Liyao Xiang +2
The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i.i.d. random variables o…
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…
MetaLoc: Learning to Learn Wireless Localization
Jun Gao, Dongze Wu, Feng Yin +3
Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather ac…
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives
Zichen Ma, Yu Lu, Wenye Li +1
Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the stat…
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
Sihua Wang, Mingzhe Chen, Christopher G. Brinton +3
This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices…
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…
Service Delay Minimization for Federated Learning over Mobile Devices
Rui Chen, Dian Shi, Xiaoqi Qin +3
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay eff…
6G Network AI Architecture for Everyone-Centric Customized Services
Yang Yang, Mulei Ma, Hequan Wu +51
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to…
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…
Federated Two-stage Learning with Sign-based Voting
Zichen Ma, Zihan Lu, Yu Lu +3
Federated learning is a distributed machine learning mechanism where local devices collaboratively train a shared global model under the orchestration of a central server, while ke…
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 LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
Yang Yang, Yujie Yang, Mingzhe Chen +4
This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC)…