From Traditional Automation to Embodied Wireless Intelligence: Vision-Language-Action Empowered Physics-Aware Communication Networks

2026-06-11

AI Summary

This work addresses the limited environmental awareness of existing wireless network automation systems, which optimize performance metrics without accounting for real-world propagation conditions. To bridge this gap, the paper introduces the embodied intelligence–enabled base station (eBS), pioneering the integration of embodied intelligence into wireless communications. The proposed system features a vision–language–action (VLA) pipeline that endows base stations with contextual perception, causal physical reasoning, and physics-aware action generation capabilities. It employs a two-layer asynchronous architecture: a semantic planner leverages state-of-the-art vision-language models to produce structured commands, while a tactical controller executes real-time adjustments. Experiments demonstrate that a single VLA pipeline—without fine-tuning—achieves zero-shot material reasoning, cross-view generalization, and dynamic event prediction prior to signal degradation, thereby advancing wireless networks from rule-driven paradigms toward embodied intelligence.

Abstract

Wireless network automation has progressed from rule-based self-organising networks (SON) to data-driven optimisation, yet existing systems remain fundamentally disembodied, optimising performance indicators without perceiving the physical environment that governs radio propagation. We propose the embodied intelligent empowered base station (eBS), a paradigm that adopts a Vision-Language-Action (VLA) pipeline to transform base stations into autonomous agents capable of situated perception, causal physical reasoning, and physics-aware action generation. The eBS employs a two-tier asynchronous architecture: a Semantic Planner powered by a frontier Vision-Language Model (VLM) generates structured action directives on human timescales, whilst a Tactical Controller executes real-time adaptation. Case studies demonstrate that a single VLA pipeline, without task-specific training, can perform zero-shot material reasoning, generalise across viewpoints, and predict dynamic events before signal degradation occurs, illustrating a paradigm shift from traditional rule-following network automation to embodied intelligence empowered future wireless networks.

Problem

• wireless network automation
• embodied intelligence
• physical environment perception
• radio propagation
• disembodied systems

Innovation

• Embodied Intelligence
• Vision-Language-Action (VLA)
• Physics-Aware Communication
• Semantic Planner
• Zero-shot Reasoning