AI 中文总结
研究针对下一代网络实现中传统机器学习和大语言模型的局限，提出基于感知-推理-行动（PERA）范式的生成网络智能，用多任务架构取代边缘模型，降低复杂性与能耗，通过案例研究验证其在链路状态分类和波束预测上的有效性。
AI 中文摘要
下一代（NG）网络的实现依赖于从预编程协议工程向自觉演进、自主和可信智能范式的根本转变。传统机器学习虽引入了局部自动化，但受限于单任务处理管道，无法处理复杂的跨层动态。大语言模型在广义认知推理方面表现出色，但在一定程度上与无线遥测的丰富模态脱节。作为解决方案，我们推出了通过感知-推理-行动（PERA）范式概念化的生成网络智能。该范式将无线信道和底层网络状态视为连续的多模态叙述。通过将大型无线人工智能模型（LWAMs）的感知基础与大语言模型的认知推理同步，PERA开创了原生NG智能时代。关键的是，这种统一智能用高效的多任务架构取代了碎片化的、特定任务的边缘模型，在支持动态物理应用所需的实时控制的同时，降低了复杂性和能耗。此外，我们对比了传统机器学习与生成范式的结构局限性，构思了跨NG协议栈的智能体推理，并详细介绍了专门为资源受限的无线边缘设计的实用三层设计。这种架构范式是在NG网络中实现完全自主、具身智能体人工智能的基础框架。为了验证这一愿景，我们的案例研究评估了链路状态分类和波束预测，展示了如何在认知引擎中为无线遥测提供支持，从而实现可信物理层诊断和波束控制所需的透明、人类可读的原理。
英文摘要
The realization of next-generation (NG) networks hinges on a fundamental departure from preprogrammed protocol engineering towards a paradigm of self-consciously evolving, autonomous and trusted intelligence. While conventional machine learning (ML) has introduced localized automation, it remains inherently bounded by single-task processing pipelines incapable of handling complex cross-layer dynamics. As a partial remedy, large language models (LLMs) excel at generalized cognitive reasoning, but to a degree they remain detached from the rich modalities of wireless telemetry. As a solution, we unveil Generative Network Intelligence conceptualized via the Perceive-Reason-Act (PERA) paradigm. This paradigm treats the wireless channel and the underlying network states as a continuous, multimodal narrative. By synchronizing the perceptual grounding of Large Wireless AI Models (LWAMs) with the cognitive reasoning of LLMs, PERA heralds the era of native NG intelligence. Crucially, this unified intelligence replaces fragmented, task-specific edge models by an efficient multi-task architecture delivering the real-time control needed for supporting dynamic physical applications while reducing both the complexity and energy dissipation. Moreover, we contrast the structural limitations of traditional ML to generative paradigms, conceive agentic reasoning across a NG protocol stack, and detail a practical three-tier design specifically engineered for the resource-constrained wireless edge. This architectural paradigm serves as a foundational framework for realizing fully autonomous, embodied agentic AI in NG networks. To validate this vision, our case study evaluates link-state classification and beam prediction, demonstrating how grounding wireless telemetry within a cognitive engine delivers the transparent, human-readable rationales required for trusted physical-layer diagnostics and beam control.
Comments9 pages, 5 figures, 1 table, magazine paper, submitted to IEEE for possible publication
