Active Inference Framework for Closed-Loop Sensing, Communication, and Control in UAV Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914043105640448 |
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| author | Pan, Guangjin Bai, Liping Tian, Zhuojun Chen, Hui Bennis, Mehdi Wymeersch, Henk |
| author_facet | Pan, Guangjin Bai, Liping Tian, Zhuojun Chen, Hui Bennis, Mehdi Wymeersch, Henk |
| contents | Integrated sensing and communication (ISAC) is a core technology for 6G, and its application to closed-loop sensing, communication, and control (SCC) enables various services. Existing SCC solutions often treat sensing and control separately, leading to suboptimal performance and resource usage. In this work, we introduce the active inference framework (AIF) into SCC-enabled unmanned aerial vehicle (UAV) systems for joint state estimation, control, and sensing resource allocation. By formulating a unified generative model, the problem reduces to minimizing variational free energy for inference and expected free energy for action planning. Simulation results show that both control cost and sensing cost are reduced relative to baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Active Inference Framework for Closed-Loop Sensing, Communication, and Control in UAV Systems Pan, Guangjin Bai, Liping Tian, Zhuojun Chen, Hui Bennis, Mehdi Wymeersch, Henk Signal Processing Networking and Internet Architecture Systems and Control Integrated sensing and communication (ISAC) is a core technology for 6G, and its application to closed-loop sensing, communication, and control (SCC) enables various services. Existing SCC solutions often treat sensing and control separately, leading to suboptimal performance and resource usage. In this work, we introduce the active inference framework (AIF) into SCC-enabled unmanned aerial vehicle (UAV) systems for joint state estimation, control, and sensing resource allocation. By formulating a unified generative model, the problem reduces to minimizing variational free energy for inference and expected free energy for action planning. Simulation results show that both control cost and sensing cost are reduced relative to baselines. |
| title | Active Inference Framework for Closed-Loop Sensing, Communication, and Control in UAV Systems |
| topic | Signal Processing Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2509.14201 |