12 citations · 22 across the 20 of their papers we have counts for
11 papers · 1 filter
Agentic-SecPBFT: Agentic AI-Driven Proactive Security Framework for Wireless PBFT Consensus in Mobile Ad-Hoc Networks
Haoxiang Luo, Yinqiu Liu, Ruichen Zhang +5
The standard Practical Byzantine Fault Tolerance (PBFT) protocol, designed for stable, wired environments, exhibits critical vulnerabilities when deployed in settings like mobile a…
Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions
Ruichen Zhang, Guangyuan Liu, Yinqiu Liu +10
The rapid expansion of sixth-generation (6G) wireless networks and the Internet of Things (IoT) has catalyzed the evolution from centralized cloud intelligence towards decentralize…
Wireless Agentic AI with Retrieval-Augmented Multimodal Semantic Perception
Guangyuan Liu, Yinqiu Liu, Ruichen Zhang +5
The rapid development of multimodal AI and Large Language Models (LLMs) has greatly enhanced real-time interaction, decision-making, and collaborative tasks. However, in wireless m…
Context-Aware Semantic Communication for the Wireless Networks
Guangyuan Liu, Yinqiu Liu, Jiacheng Wang +5
In next-generation wireless networks, supporting real-time applications such as augmented reality, autonomous driving, and immersive Metaverse services demands stringent constraint…
LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach
Yinqiu Liu, Guangyuan Liu, Jiacheng Wang +5
Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, co…
Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse
Guangyuan Liu, Hongyang Du, Jiacheng Wang +2
The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation…
Adaptive Contextual Caching for Mobile Edge Large Language Model Service
Guangyuan Liu, Yinqiu Liu, Jiacheng Wang +4
Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation…
Hallucination-aware Optimization for Large Language Model-empowered Communications
Yinqiu Liu, Guangyuan Liu, Ruichen Zhang +5
Large Language Models (LLMs) have significantly advanced communications fields, such as Telecom Q\&A, mathematical modeling, and coding. However, LLMs encounter an inherent issue k…
Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion
Yinqiu Liu, Guangyuan Liu, Hongyang Du +5
In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning ze…
Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse
Guangyuan Liu, Hongyang Du, Dusit Niyato +5
In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However,…
Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach
Hongyang Du, Guangyuan Liu, Yijing Lin +4
Optimizing various wireless user tasks poses a significant challenge for networking systems because of the expanding range of user requirements. Despite advancements in Deep Reinfo…