Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications, short-term human pose prediction facilitates continuous human tracking and resource allocation in advance. In this paper, we propose a Cramer-Rao bound (CRB) guided resource allocation framework for indoor mmWave ISCC systems to minimize the human pose prediction error under communication, latency, and energy constraints. We characterize the impact of sensing power on range-estimation uncertainty and point-cloud perturbation based on the CRB. To capture the impact of computation resources on prediction performance, we adopt an adaptive-depth Mamba-based pose prediction model, where lightweight prediction heads are attached after every layer to enable inference with different model depths. With this unified sensing-computation modeling, we establish a quantitative relationship among sensing power, model depth, and prediction error. Furthermore, we formulate a joint resource allocation problem to minimize the pose prediction error. To solve this problem efficiently, we develop an alternating optimization (AO)-based algorithm, where closed-form solutions are derived for the sensing power and model depth update steps. Simulation results show that the proposed scheme significantly reduces pose prediction error compared with baseline methods, validating its effectiveness for resource-constrained indoor human-centric ISCC systems.

翻译：集成感知、通信与计算（ISCC）为室内以人为中心的应用提供了一种有前景的框架。在此类应用中，短期人体姿态预测有助于提前实现连续人体跟踪和资源分配。本文提出一种面向室内毫米波ISCC系统的克拉美罗界（CRB）引导资源分配框架，旨在通信、时延和能量约束下最小化人体姿态预测误差。基于CRB，我们刻画了感知功率对距离估计不确定性和点云扰动的影响。为捕获计算资源对预测性能的影响，我们采用自适应深度的Mamba姿态预测模型，该模型在每一层后附加轻量级预测头，使推理能够适应不同模型深度。通过这种统一的感知-计算建模，我们建立了感知功率、模型深度与预测误差之间的量化关系。此外，我们构建了一个联合资源分配问题以最小化姿态预测误差。为高效求解该问题，我们开发了基于交替优化（AO）的算法，其中感知功率和模型深度更新步骤均推导出闭式解。仿真结果表明，与基线方法相比，所提方案显著降低了姿态预测误差，验证了其在资源受限的室内以人为中心ISCC系统中的有效性。