58 citations · 296 across the 51 of their papers we have counts for
52 papers
Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey
Qianzhou Chen, Siqi Sun, Minrui Xu +6
The rapid advancements in foundation models and sixth-generation (6G) wireless communication systems necessitate the development of efficient, scalable, and privacy-preserving mach…
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…
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…
Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing
Yue Xiu, Yang Zhao, Songjie Yang +4
In future 6G networks, anti-jamming will become a critical challenge, particularly with the development of intelligent jammers that can initiate malicious interference, posing a si…
Hyperdimensional Computing Empowered Federated Foundation Model over Wireless Networks for Metaverse
Yahao Ding, Wen Shang, Minrui Xu +4
The Metaverse, a burgeoning collective virtual space merging augmented reality and persistent virtual worlds, necessitates advanced artificial intelligence (AI) and communication t…
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses
Yongju Tong, Jiawen Kang, Junlong Chen +4
Air-ground integrated networks can relieve communication pressure on ground transportation networks and provide 6G-enabled vehicular Metaverses services offloading in remote areas…
Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach
Yongju Tong, Junlong Chen, Minrui Xu +5
Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles and roadside infrastructures, e.g., Road…
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…
Fluid Antenna Relay Assisted Communication Systems Through Antenna Location Optimization
Ruopeng Xu, Yixuan Chen, Jiawen Kang +4
In this paper, we investigate the problem of resource allocation for fluid antenna relay (FAR) system with antenna location optimization. In the considered model, each user transmi…
Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand
Jiani Fan, Minrui Xu, Jiale Guo +4
The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through…
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks
Bingkun Lai, Jiayi He, Jiawen Kang +4
Generative Artificial Intelligence (GAI) shows remarkable productivity and creativity in Mobile Edge Networks, such as the metaverse and the Industrial Internet of Things. Federate…
A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles
Jiani Fan, Minrui Xu, Ziyao Liu +4
Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV)…
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…
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment
Minrui Xu, Dusit Niyato, Jiawen Kang +5
AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various do…
Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving
Wensheng Su, Zhenni Li, Minrui Xu +3
Deep reinforcement learning (DRL) has shown remarkable success in complex autonomous driving scenarios. However, DRL models inevitably bring high memory consumption and computation…
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…
Generative AI-enabled Mobile Tactical Multimedia Networks: Distribution, Generation, and Perception
Minrui Xu, Dusit Niyato, Jiawen Kang +4
Mobile multimedia networks (MMNs) demonstrate great potential in delivering low-latency and high-quality entertainment and tactical applications, such as short-video sharing, onlin…
Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach
Jiawen Kang, Yue Zhong, Minrui Xu +7
The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses, which create a unified ecosystem that blends physica…
Learning-based Incentive Mechanism for Task Freshness-aware Vehicular Twin Migration
Junhong Zhang, Jiangtian Nie, Jinbo Wen +4
Vehicular metaverses are an emerging paradigm that integrates extended reality technologies and real-time sensing data to bridge the physical space and digital spaces for intellige…