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

• URL: http://arxiv.org/abs/2512.15044v1
• Date: Wed, 17 Dec 2025 03:16:06 GMT
• Title: Agentic AI for Integrated Sensing and Communication: Analysis, Framework, and Case Study
• Abstract summary: Integrated sensing and communication (ISAC) has emerged as a key development direction in the sixth-generation (6G) era.<n>We show several common optimization approaches for ISAC systems and highlight the significant advantages of generative artificial intelligence (GenAI)-based agentic AI.
• Score: 45.7534656284555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• When Agentic AI Meets Integrated Sensing and Communication [23.084474668808912]
  Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) into a goal-driven, closed-loop intelligent system.<n>This survey unifies the literature within a six-stage closed-loop framework.<n>It introduces five levels of agentic maturity, ranging from physical-layer primitives to fully closed-loop ISAC.
  arXiv  Detail & Related papers  (2026-08-06T09:26:49Z)
• Agentic AI for Mobile Network RAN Management and Optimization [0.0]
  Agentic AI represents a new paradigm for automating complex systems by using Large AI Models (LAMs)<n>This paper contributes to ongoing research on Agentic AI in 5G and 6G networks by tracing its evolution from classical agents to Agentic AI.<n>Core design patterns-reflection, planning, tool use, and multi-agent collaboration-are then described to illustrate how intelligent behaviors are orchestrated.
  arXiv  Detail & Related papers  (2025-11-04T12:34:57Z)
• From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
  Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
  arXiv  Detail & Related papers  (2025-10-07T05:45:25Z)
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems [53.37728204835912]
  Most existing AI systems rely on manually crafted configurations that remain static after deployment.<n>Recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback.<n>This survey aims to provide researchers and practitioners with a systematic understanding of self-evolving AI agents.
  arXiv  Detail & Related papers  (2025-08-10T16:07:32Z)
• Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches [76.12691010182802]
  This survey focuses on enabling agentic artificial intelligence (AI) in satellite-augmented low-altitude economy and terrestrial networks (SLAETNs)<n>We introduce the architecture and characteristics of SLAETNs, and analyze the challenges that arise in integrating satellite, aerial, and terrestrial components.<n>We examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks.
  arXiv  Detail & Related papers  (2025-07-19T14:07:05Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Agentic AI Optimisation (AAIO): what it is, how it works, why it matters, and how to deal with it [1.8390952204639035]
  This article introduces Agentic AI optimisation (AAIO) as an essential methodology for ensuring effective integration between websites and agentic AI systems.<n>By examining the mutual interdependency between website optimisation and agentic AI success, it highlights the virtuous cycle that AAIO can create.<n>The article concludes by affirming AAIO's essential role as part of a fundamental digital infrastructure in the era of autonomous digital agents, advocating for equitable and inclusive access to its benefits.
  arXiv  Detail & Related papers  (2025-04-16T20:38:09Z)
• Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach [35.05793485239977]
  We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents.<n>We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet.
  arXiv  Detail & Related papers  (2025-03-20T00:48:44Z)
• Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
  AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users.<n>Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources.<n>We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
  arXiv  Detail & Related papers  (2025-02-17T03:05:20Z)
• Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions [16.28550500194823]
  fusion of Artificial Intelligence with Operating Systems emerges as a critical frontier for innovation.<n>Current status of AI-OS integration, accentuating its pivotal role in steering the evolution of advanced computing paradigms.<n>Future prospects of Intelligent Operating Systems, debating how groundbreaking OS designs will usher in novel possibilities.
  arXiv  Detail & Related papers  (2024-07-19T05:29:34Z)
• Autonomous Crowdsensing: Operating and Organizing Crowdsensing for Sensing Automation [18.73616489977625]
  Crowdsensing Intelligence (CSI) has been proposed to collect data from Cyber-Physical-Social Systems. This letter reports the outcomes of the latest Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence.
  arXiv  Detail & Related papers  (2024-01-06T14:41:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.