让大模型主动感知环境，实现无线系统自适应运行
Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems
- 构建能主动探测环境的无线大模型，突破静态数据依赖
- 通过案例验证其在动态环境中提升系统响应能力
- 适合研究智能通信与自主网络的学者参考
大人工智能模型为未来无线系统带来革命性潜力，可实现前所未有的网络优化与性能提升。然而现有范式普遍忽视关键的物理交互，主要依赖离线数据集，难以应对实时无线动态与非平稳环境，且缺乏主动环境探测能力。本文提出一种根本性范式转变：无线具身大模型（WELAI），从被动观测转向主动具身。首先识别现有模型的关键挑战，进而探索WELAI的设计原则与系统架构。此外，概述其在下一代无线系统中的潜在应用。最后，通过一个示范性案例研究，验证WELAI的有效性，并指出实现自适应、鲁棒、自治无线系统的有前景研究方向。
原文摘要 · Abstract (English)
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.
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