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

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 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.