5 papers
CORE:Toward Ubiquitous 6G Intelligence Through Collaborative Orchestration of Large Language Model Agents Over Hierarchical Edge
Zitong Yu, Boquan Sun, Yang Li +2
Rapid advancements in sixth-generation (6G) networks and large language models (LLMs) have paved the way for ubiquitous intelligence, wherein seamless connectivity and distributed…
LLM Enabled Multi-Agent System for 6G Networks: Framework and Method of Dual-Loop Edge-Terminal Collaboration
Zheyan Qu, Wenbo Wang, Zitong Yu +3
The ubiquitous computing resources in 6G networks provide ideal environments for the fusion of large language models (LLMs) and intelligent services through the agent framework. Wi…
Incentive-Driven Task Offloading and Collaborative Computing in Device-Assisted MEC Networks
Yang Li, Xing Zhang, Bo Lei +4
Edge computing (EC), positioned near end devices, holds significant potential for delivering low-latency, energy-efficient, and secure services. This makes it a crucial component o…
Priority and Stackelberg Game-Based Incentive Task Allocation for Device-Assisted MEC Networks
Yang Li, Xing Zhang, Bo Lei +2
Mobile edge computing (MEC) is a promising computing paradigm that offers users proximity and instant computing services for various applications, and it has become an essential co…
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization
Zheyan Qu, Lu Yin, Zitong Yu +2
Large language models (LLMs) have demonstrated astonishing capabilities in natural language processing (NLP) tasks, sparking interest in their application to professional domains w…