Agentic AI for Integrated Sensing and Communication: Analysis, Framework, and Case Study
This addresses the need for efficient and adaptable ISAC systems in 6G networks, but it appears incremental as it builds on existing agentic AI and ISAC concepts.
The paper tackles the challenge of making Integrated Sensing and Communication (ISAC) systems more intelligent and autonomous in dynamic wireless environments by proposing a novel agentic AI framework, which is verified through a case study to optimize ISAC performance.
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.