Agentic AI for Integrated Sensing and Communication: Analysis, Framework, and Case Study

2025-12-16

AI Summary

To address the insufficient autonomy and adaptability of Integrated Sensing and Communication (ISAC) systems in dynamic, complex 6G wireless environments, this paper proposes, for the first time, a generative AI-driven embodied intelligent agent framework for ISAC, establishing a closed-loop “perception–reasoning–decision” architecture. The framework tightly integrates large language models (LLMs), reinforcement learning, multi-agent coordination, digital twin simulation, and real-time perception feedback to enable end-to-end adaptive resource orchestration and cross-modal joint optimization. Experimental results demonstrate that, compared with baseline approaches, the proposed framework achieves a 23% improvement in communication throughput and a 31% gain in sensing accuracy, while significantly enhancing system robustness and generalization capability. This work establishes a novel paradigm for the evolution of ISAC toward embodiment and intelligence.

Technology Category

• Multiagent Systems: Agent Communication
• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Enhancing ISAC system intelligence in dynamic environments
• Applying agentic AI for autonomous ISAC operation optimization
• Proposing a novel agentic ISAC framework with case validation

Innovation

• Agentic AI enables continuous perception-reasoning-action loops
• Proposes a novel agentic ISAC framework for optimization
• Uses generative AI-based agents to enhance ISAC performance