58 citations · 296 across the 51 of their papers we have counts for
19 papers · 1 filter
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…
Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses
Jiawen Kang, Yongju Tong, Yue Zhong +5
The rise of 6G-enable Vehicular Metaverses is transforming the automotive industry by integrating immersive, real-time vehicular services through ultra-low latency and high bandwid…
Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey
Minrui Xu, Dusit Niyato, Jiawen Kang +6
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, com…
Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks
Minrui Xu, Dusit Niyato, Hongliang Zhang +4
Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-late…
Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation
Minrui Xu, Dusit Niyato, Jiawen Kang +5
Quantum computing networks enable scalable collaboration and secure information exchange among multiple classical and quantum computing nodes while executing large-scale generative…
Joint Foundation Model Caching and Inference of Generative AI Services for Edge Intelligence
Minrui Xu, Dusit Niyato, Hongliang Zhang +4
With the rapid development of artificial general intelligence (AGI), various multimedia services based on pretrained foundation models (PFMs) need to be effectively deployed. With…
Sparks of GPTs in Edge Intelligence for Metaverse: Caching and Inference for Mobile AIGC Services
Minrui Xu, Dusit Niyato, Hongliang Zhang +4
Aiming at achieving artificial general intelligence (AGI) for Metaverse, pretrained foundation models (PFMs), e.g., generative pretrained transformers (GPTs), can effectively provi…
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services
Minrui Xu, Hongyang Du, Dusit Niyato +9
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This s…
AI-Generated Network Design: A Diffusion Model-based Learning Approach
Yudong Huang, Minrui Xu, Xinyuan Zhang +4
The future networks pose intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirem…
EPViSA: Efficient Auction Design for Real-time Physical-Virtual Synchronization in the Metaverse
Minrui Xu, Dusit Niyato, Benjamin Wright +5
Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no lo…
Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing
Minrui Xu, Dusit Niyato, Jiawen Kang +2
In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile use…
Holographic-Type Communication for Digital Twin: A Learning-based Auction Approach
XiuYu Zhang, Minrui Xu, Rui Tan +1
Digital Twin (DT) technologies, which aim to build digital replicas of physical entities, are the key to providing efficient, concurrent simulation and analysis of real-world objec…
Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet
Minrui Xu, Dusit Niyato, Zhaohui Yang +5
Federated edge learning (FEL) is a promising paradigm of distributed machine learning that can preserve data privacy while training the global model collaboratively. However, FEL i…
A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges
Minrui Xu, Wei Chong Ng, Wei Yang Bryan Lim +6
Dubbed "the successor to the mobile Internet", the concept of the Metaverse has grown in popularity. While there exist lite versions of the Metaverse today, they are still far from…
Secure and Reliable Transfer Learning Framework for 6G-enabled Internet of Vehicles
Minrui Xu, Dinh Thai Hoang, Jiawen Kang +3
In the coming 6G era, Internet of Vehicles (IoV) has been evolving towards 6G-enabled IoV with super-high data rate, seamless networking coverage, and ubiquitous intelligence by Ar…