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This paper proposes a Cramer-Rao bound (CRB) guided resource allocation framework for indoor mmWave integrated sensing, communication, and computation (ISCC) systems, aiming to minimize human pose prediction error under resource constraints. They model the impact of sensing power on range estimation and point cloud perturbation using the CRB, and adopt an adaptive-depth Mamba model to relate computation resources to prediction performance. An alternating optimization algorithm is developed to solve the joint resource allocation problem, demonstrating significant reduction in pose prediction error compared to baselines.
Optimizing sensing power and Mamba model depth using a CRB-guided approach slashes human pose prediction error in resource-constrained indoor ISCC systems.
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.