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.

翻译：集成感知与通信（ISAC）是6G的核心技术，其在闭环感知、通信与控制（SCC）中的应用能够支持多种服务。现有的SCC方案通常将感知与控制分开处理，导致性能与资源利用欠佳。本研究将主动推理框架（AIF）引入支持SCC的无人机（UAV）系统，以实现联合状态估计、控制与感知资源分配。通过构建统一的生成模型，该问题可归结为在推理过程中最小化变分自由能，并在动作规划中最小化期望自由能。仿真结果表明，相较于基线方法，所提方案同时降低了控制成本与感知成本。