58 citations · 296 across the 51 of their papers we have counts for
6 papers · 1 filter
A QoE-Driven Personalized Incentive Mechanism Design for AIGC Services in Resource-Constrained Edge Networks
Hongjia Wu, Minrui Xu, Zehui Xiong +4
With rapid advancements in large language models (LLMs), AI-generated content (AIGC) has emerged as a key driver of technological innovation and economic transformation. Personaliz…
Forewarned is Forearmed: A Survey on Large Language Model-based Agents in Autonomous Cyberattacks
Minrui Xu, Jiani Fan, Xinyu Huang +8
With the continuous evolution of Large Language Models (LLMs), LLM-based agents have advanced beyond passive chatbots to become autonomous cyber entities capable of performing comp…
Shadow Wireless Intelligence: Large Language Model-Driven Reasoning in Covert Communications
Yuanai Xie, Zhaozhi Liu, Xiao Zhang +5
Covert Communications (CC) can secure sensitive transmissions in industrial, military, and mission-critical applications within 6G wireless networks. However, traditional optimizat…
Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing
Minrui Xu, Dusit Niyato, Jiawen Kang +5
Exploiting quantum computing at the mobile edge holds immense potential for facilitating large-scale network design, processing multimodal data, optimizing resource management, and…
Efficient Twin Migration in Vehicular Metaverses: Multi-Agent Split Deep Reinforcement Learning with Spatio-Temporal Trajectory Generation
Junlong Chen, Jiawen Kang, Minrui Xu +5
Vehicle Twins (VTs) as digital representations of vehicles can provide users with immersive experiences in vehicular metaverse applications, e.g., Augmented Reality (AR) navigation…
Serving Long-Context LLMs at the Mobile Edge: Test-Time Reinforcement Learning-based Model Caching and Inference Offloading
Minrui Xu, Dusit Niyato, Christopher G. Brinton
Large Language Models (LLMs) can perform zero-shot learning on unseen tasks and few-shot learning on complex reasoning tasks. However, resource-limited mobile edge networks struggl…