3 citations · 8 across the 11 of their papers we have counts for
7 papers · 1 filter
LLM-Slice: Dedicated Wireless Network Slicing for Large Language Models
Boyi Liu, Jingwen Tong, Jun Zhang
The rapid adoption of large language models (LLMs) presents new challenges for existing network architectures due to significant peak traffic and high communication uncertainty. Tr…
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Jingwen Tong, Jiawei Shao, Qiong Wu +4
Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particular…
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
Jiawei Shao, Jingwen Tong, Qiong Wu +4
The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configur…
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation
Jingwen Tong, Xinran Li, Liqun Fu +2
Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dyn…
EdgeLoc: A Communication-Adaptive Parallel System for Real-Time Localization in Infrastructure-Assisted Autonomous Driving
Boyi Liu, Jingwen Tong, Yufan Zhuang
This paper presents EdgeLoc, an infrastructure-assisted, real-time localization system for autonomous driving that addresses the incompatibility between traditional localization me…
Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds
Liqun Fu, Jingwen Tong, Tongtong Lin +1
As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple chal…
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
Jingwen Tong, Zhenzhen Chen, Liqun Fu +2
Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user exp…