6 papers
Agent-Native Task-Oriented Communication with Joint Token Compression Coding and Modulation
Zhuoran Xiao, Yihang Huang, Tianyu Jiao +2
As large foundation models empower agents to become pervasive across industries and emerge as central actors in intelligent systems, a fundamental rethinking of communication parad…
Towards Native Intelligence: 6G-LLM Trained with Reinforcement Learning from NDT Feedback
Zhuoran Xiao, Tao Tao, Chenhui Ye +4
Owing to its comprehensive understanding of upper-layer application requirements and the capabilities of practical communication systems, the 6G-LLM (6G domain large language model…
Transmission With Machine Language Tokens: A Paradigm for Task-Oriented Agent Communication
Zhuoran Xiao, Chenhui Ye, Yijia Feng +4
The rapid advancement in large foundation models is propelling the paradigm shifts across various industries. One significant change is that agents, instead of traditional machines…
AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model
Tianyu Jiao, Zhuoran Xiao, Yihang Huang +9
Designing a 6G-oriented universal model capable of processing multi-modal data and executing diverse air interface tasks has emerged as a common goal in future wireless systems. Bu…
Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm
Tianyu Jiao, Zhuoran Xiao, Yin Xu +9
Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communicatio…
LLM Agents as 6G Orchestrator: A Paradigm for Task-Oriented Physical-Layer Automation
Zhuoran Xiao, Chenhui Ye, Yunbo Hu +5
The rapid advancement in generative pre-training models is propelling a paradigm shift in technological progression from basic applications such as chatbots towards more sophistica…