5 citations · 18 across the 16 of their papers we have counts for
7 papers · 1 filter
Smart-TCP: An Agentic AI-based Autonomous and Adaptive TCP Protocol
Yule Han, Kezhi Wang, Yizhe Zhao +1
The Transmission Control Protocol (TCP) relies on deterministic state machines and sequence-number arithmetic to ensure reliable communication. However, hard-coded protocol logic i…
SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks
Lin Zhu, Kezhi Wang, Luping Xiang +1
Efficient information exchange and reliable contextual reasoning are essential for vehicle-to-everything (V2X) networks. Conventional communication schemes often incur significant…
A Unified Cloud-Edge-Terminal Framework for Multimodal Integrated Sensing and Communication
Yubo Peng, Luping Xiang, Kun Yang +3
The transition to 6G calls for tightly integrated sensing and communication to support mission-critical services such as autonomous driving, embodied AI, and high-precision telemed…
SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework
Yubo Peng, Luping Xiang, Kun Yang +3
Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constra…
CommGPT: A Graph and Retrieval-Augmented Multimodal Communication Foundation Model
Feibo Jiang, Wanyun Zhu, Li Dong +4
Large Language Models (LLMs) possess human-level cognitive and decision-making capabilities, making them a key technology for 6G. However, applying LLMs to the communication domain…
FlashSAM: Lightweight Vision Model for Multi-UAV Token Communication in Low-Latitude Wireless Networks
Feibo Jiang, Siwei Tu, Li Dong +5
Token Communication (TokenCom) is a promising paradigm for low-altitude wireless networks, as it focuses on transmitting task-relevant core information, particularly in environment…
M4SC: An MLLM-based Multi-modal, Multi-task and Multi-user Semantic Communication System
Feibo Jiang, Siwei Tu, Li Dong +3
Multi-modal Large Language Models (MLLMs) are capable of precisely extracting high-level semantic information from multi-modal data, enabling multi-task understanding and generatio…