arXiv 2512.151092025-12-22eess.SPcs.AIcs.ITmath.IT

大模型赋能的具身智能用于6G集成感知、通信与计算网络

Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network

School of Information and Electronics（信息与电子学院）；School of Interdisciplinary Science（交叉科学学院）；State Key Laboratory of CNS/ATM（CNS/ATM国家重点实验室）；Beijing Institute of Technology（北京理工大学）；Advanced Technology Research Institute（先进技术研究院）；Yangtze Delta Region Academy（长江三角洲地区学院）；School of School of Information Science and Engineering（信息科学与工程学院）；Institute of Intelligent Communication Technologies（智能通信技术研究院）；Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration（智能通信与传感-计算集成山东省重点实验室）；Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking（信息处理、通信与网络浙江省重点实验室）；Department of Electronic Engineering（电子工程系）；State Key Laboratory of Space Network and Communications（空间网络与通信国家重点实验室）；National Mobile Communications Research Laboratory（移动通信研究中心）；Purple Mountain Laboratories（紫金山实验室）

Zhuoran Li, Zhen Gao, Xinhua Liu, Zheng Wang, Xiaotian Zhou, Lei Liu, Yongpeng Wu, Wei Feng, Yongming Huang

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AI总结：

本文提出利用大模型赋能基站实现感知、通信和计算一体化，为6G系统提供安全关键的智能解决方案。

摘要：

第六代(6G)的出现将智能置于无线架构的核心，将感知、通信和计算融合为一个闭环系统。本文认为，大人工智能模型(LAMs)可以使基站具备感知、推理和行动能力，从而将它们转变为智能基站代理(IBSAs)。我们首先回顾了基站从单一功能的模拟基础设施到分布式、软件定义，最终到由LAMs赋能的IBSA的历史演变，突出了伴随的架构、硬件平台和部署变化。随后，我们提出了一种将感知-认知-执行管道与云-边-端协作和参数高效适应相结合的IBSA架构。接着，我们研究了两个代表性场景：(i)协作车辆-道路感知用于自动驾驶，以及(ii)无处不在的基站支持低空无人 aerial 车辆安全监控和应对未经授权的无人机。在此基础上，我们分析了涵盖LAM设计与训练、高效的边缘-云推理、多模态感知与行动、以及可信的安全与治理等关键使能技术。我们进一步提出了一个涵盖通信性能、感知精度、决策可靠性、安全性和能效的综合评估框架和基准考虑因素。最后，我们总结了关于基准、持续适应、可信决策和标准化等开放挑战。一起，这项工作将LAM赋能的IBSA定位为实现集成感知、通信和计算原生、安全关键的6G系统的一种实际途径。

英文摘要：

The advent of sixth-generation (6G) places intelligence at the core of wireless architecture, fusing perception, communication, and computation into a single closed-loop. This paper argues that large artificial intelligence models (LAMs) can endow base stations with perception, reasoning, and acting capabilities, thus transforming them into intelligent base station agents (IBSAs). We first review the historical evolution of BSs from single-functional analog infrastructure to distributed, software-defined, and finally LAM-empowered IBSA, highlighting the accompanying changes in architecture, hardware platforms, and deployment. We then present an IBSA architecture that couples a perception-cognition-execution pipeline with cloud-edge-end collaboration and parameter-efficient adaptation. Subsequently,we study two representative scenarios: (i) cooperative vehicle-road perception for autonomous driving, and (ii) ubiquitous base station support for low-altitude uncrewed aerial vehicle safety monitoring and response against unauthorized drones. On this basis, we analyze key enabling technologies spanning LAM design and training, efficient edge-cloud inference, multi-modal perception and actuation, as well as trustworthy security and governance. We further propose a holistic evaluation framework and benchmark considerations that jointly cover communication performance, perception accuracy, decision-making reliability, safety, and energy efficiency. Finally, we distill open challenges on benchmarks, continual adaptation, trustworthy decision-making, and standardization. Together, this work positions LAM-enabled IBSAs as a practical path toward integrated perception, communication, and computation native, safety-critical 6G systems.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。