Large Model Enabled Embodied Intelligence for 6G Integrated Perception, Communication, and Computation Network
Zhuoran Li, Zhen Gao, Xinhua Liu, Zheng Wang, Xiaotian Zhou, Lei Liu, Yongpeng Wu, Wei Feng, Yongming Huang

TL;DR
This paper explores how large AI models can transform 6G base stations into intelligent agents capable of perception, reasoning, and acting, enabling integrated perception, communication, and computation for safety-critical applications.
Contribution
It introduces an IBSA architecture leveraging large models, reviews historical evolution, and presents scenarios, technologies, and evaluation frameworks for LAM-enabled 6G systems.
Findings
LAMs enable perception, reasoning, and acting in base stations.
Proposed architecture couples perception, cognition, and execution with cloud-edge collaboration.
Benchmarking covers communication, perception, decision-making, safety, and energy efficiency.
Abstract
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…
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Taxonomy
TopicsUAV Applications and Optimization · Advanced Wireless Communication Technologies · Vehicular Ad Hoc Networks (VANETs)
