Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914495478104064 |
|---|---|
| author | Pan, Guangjin Tian, Zhuojun Bennis, Mehdi Wymeersch, Henk |
| author_facet | Pan, Guangjin Tian, Zhuojun Bennis, Mehdi Wymeersch, Henk |
| contents | Wireless agentic systems enable agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop ISAC, which jointly optimizes control and sensing resource allocation via backward--forward message passing on a factor graph. The AIF agent maintains a generative model as a digital twin by integrating a localization model for uncertainty-aware state inference and a localization channel knowledge map (CKM) for approximating observation quality during planning. Simulation results demonstrate that the AIF-enabled agent adaptively allocates sensing resources based on spatially varying channel conditions, achieving superior balance among tracking accuracy, control effort, and sensing resource consumption over baseline strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19599 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning Pan, Guangjin Tian, Zhuojun Bennis, Mehdi Wymeersch, Henk Signal Processing Networking and Internet Architecture Wireless agentic systems enable agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop ISAC, which jointly optimizes control and sensing resource allocation via backward--forward message passing on a factor graph. The AIF agent maintains a generative model as a digital twin by integrating a localization model for uncertainty-aware state inference and a localization channel knowledge map (CKM) for approximating observation quality during planning. Simulation results demonstrate that the AIF-enabled agent adaptively allocates sensing resources based on spatially varying channel conditions, achieving superior balance among tracking accuracy, control effort, and sensing resource consumption over baseline strategies. |
| title | Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2604.19599 |