用智能代理AI提升6G感知通信系统的自主性与效率
Agentic AI for ISAC: Analysis, Framework, and Case Study
- 构建基于生成式AI的智能代理框架，实现感知-推理-决策闭环
- 案例验证该框架显著优化了系统性能，适应动态无线环境
- 适合研究6G智能网络与自主系统设计的学者与工程师
集成感知与通信（ISAC）已成为第六代移动通信（6G）时代的关键发展方向，为未来智能网络的协同感知与通信提供支撑。然而，随着无线环境日益动态复杂，ISAC系统需更强的智能处理能力与自主运行能力以维持高效性与适应性。与此同时，智能体人工智能（Agentic AI）通过在动态环境中实现持续的感知-推理-行动循环，为解决上述挑战提供了可行方案。本文深入分析了智能体AI在ISAC系统中的应用价值与前景：首先，全面回顾了智能体AI与ISAC系统的核心特性；其次，梳理了常见的ISAC优化方法，并凸显基于生成式AI（GenAI）的智能体AI显著优势；再次，提出一种新型智能体ISAC框架，并通过案例研究验证其在提升系统性能方面的优越性；最后，明确了基于智能体AI的ISAC系统未来研究方向。
原文摘要 · Abstract (English)
Integrated sensing and communication (ISAC) has emerged as a key development direction in the sixth-generation (6G) era, which provides essential support for the collaborative sensing and communication of future intelligent networks. However, as wireless environments become increasingly dynamic and complex, ISAC systems require more intelligent processing and more autonomous operation to maintain efficiency and adaptability. Meanwhile, agentic artificial intelligence (AI) offers a feasible solution to address these challenges by enabling continuous perception-reasoning-action loops in dynamic environments to support intelligent, autonomous, and efficient operation for ISAC systems. As such, we delve into the application value and prospects of agentic AI in ISAC systems in this work. Firstly, we provide a comprehensive review of agentic AI and ISAC systems to demonstrate their key characteristics. Secondly, we show several common optimization approaches for ISAC systems and highlight the significant advantages of generative artificial intelligence (GenAI)-based agentic AI. Thirdly, we propose a novel agentic ISAC framework and prensent a case study to verify its superiority in optimizing ISAC performance. Finally, we clarify future research directions for agentic AI-based ISAC systems.
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