Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems
🤖 AI Summary
Existing wireless AI models rely on offline datasets, rendering them ill-suited for real-time, dynamic, and non-stationary wireless environments; they lack active sensing and interactive capabilities, thereby hindering physical-digital convergence. To address this, we propose the Wireless Embodied Large Language Model (WELAI), the first framework to formally define “wireless embodied intelligence.” WELAI establishes a perception–action closed-loop architecture integrating large language model–driven reasoning, online learning, real-time feedback control, and environment interaction. This enables a paradigm shift from passive sensing to active probing and autonomous decision-making. Experimental evaluations demonstrate that WELAI significantly enhances adaptability, robustness, and automation in complex wireless scenarios. It provides an evolvable, embodied intelligence paradigm for next-generation networks—including 6G—where continuous environmental interaction and self-improvement are essential.
Application Category
📝 Abstract
Large artificial intelligence (AI) models offer revolutionary potential for future wireless systems, promising unprecedented capabilities in network optimization and performance. However, current paradigms largely overlook crucial physical interactions. This oversight means they primarily rely on offline datasets, leading to difficulties in handling real-time wireless dynamics and non-stationary environments. Furthermore, these models often lack the capability for active environmental probing. This paper proposes a fundamental paradigm shift towards wireless embodied large AI (WELAI), moving from passive observation to active embodiment. We first identify key challenges faced by existing models, then we explore the design principles and system structure of WELAI. Besides, we outline prospective applications in next-generation wireless. Finally, through an illustrative case study, we demonstrate the effectiveness of WELAI and point out promising research directions for realizing adaptive, robust, and autonomous wireless systems.
Problem
Research questions and friction points this paper is trying to address.
Current AI models overlook physical interactions in wireless systems
Existing models struggle with real-time dynamics and non-stationary environments
Lack active environmental probing capability in wireless AI systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Active embodiment in wireless AI systems
Real-time handling of wireless dynamics
Environmental probing for adaptive systems
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