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

2025-12-17

AI Summary

To address the lack of perception–cognition–action closed-loop capability in conventional base stations for 6G-native intelligence, this paper proposes a Large Model–Empowered Intelligent Base Station Agent (IBSA). Methodologically, it pioneers the embodiment intelligence paradigm for 6G base stations, establishing a cloud-edge-device collaborative and parameter-efficient IBSA architecture. The design integrates lightweight fine-tuning of large language and multimodal models, multimodal perception and actuation interfaces, trustworthy security governance, and dynamic scenario adaptation. A unified evaluation framework is introduced, covering communications, sensing, decision-making, security, and energy efficiency. Experimental validation in vehicle-infrastructure cooperation and low-altitude UAV monitoring scenarios demonstrates significant improvements: +18.7% sensing accuracy, 42% reduction in response latency, and enhanced system security. This work delivers a practical, deployable technical pathway toward 6G intelligent-native networks.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Large AI models enable intelligent base stations for 6G
• Integrate perception, communication, and computation into a closed-loop
• Apply to autonomous driving and UAV safety monitoring scenarios

Innovation

• Large AI models enable intelligent base station agents
• Architecture couples perception-cognition-execution with cloud-edge-end collaboration
• Proposes holistic evaluation framework covering communication, perception, and safety