用大模型实现智能通信系统，让机器自适应协同工作。
Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges
- 用大模型整合多模态信息，动态调整通信策略。
- 实测显示性能远超传统方法，在复杂环境下更稳定。
- 适合6G智能通信、机器人协同等前沿场景研究者参考。
大型语言模型（LLMs）和大型多模态模型（LMMs）在自然语言理解、生成与复杂推理方面取得突破性进展，展现出推动6G机器间、车辆与人形机器人自主通信的巨大潜力。本文综述了基于LLM/LMM的任务导向自主通信框架，聚焦多模态感知融合、自适应重构及无线任务的提示/微调策略。通过三个案例验证：基于LMM的交通管控、基于LLM的机器人调度、基于LMM的环境感知信道估计。实验表明，所提的LLM/LMM辅助自主系统显著优于传统及判别式深度学习模型，在动态目标、参数变化与异构多模态条件下仍保持鲁棒性，而传统静态优化方法性能明显下降。
原文摘要 · Abstract (English)
Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this article, we provide an overview of task-oriented autonomous communications with LLMs/LMMs, focusing on multimodal sensing integration, adaptive reconfiguration, and prompt/fine-tuning strategies for wireless tasks. We demonstrate the framework through three case studies: LMM-based traffic control, LLM-based robot scheduling, and LMM-based environment-aware channel estimation. From experimental results, we show that the proposed LLM/LMM-aided autonomous systems significantly outperform conventional and discriminative deep learning (DL) model-based techniques, maintaining robustness under dynamic objectives, varying input parameters, and heterogeneous multimodal conditions where conventional static optimization degrades.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。