CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systems

2026-05-28

AI Summary

This work addresses the challenge of minimizing human pose prediction error in indoor millimeter-wave communication, sensing, and computing integrated systems under constraints on communication resources, latency, and energy consumption. To this end, the authors propose a Cramér–Rao Bound (CRB)-guided joint resource allocation framework that, for the first time, integrates the CRB with an adaptive deep Mamba model to establish a quantitative relationship among sensing power, model depth, and prediction accuracy. An alternating optimization algorithm is developed to efficiently solve the resulting joint optimization problem. Simulation results demonstrate that, under resource-constrained conditions, the proposed approach significantly outperforms existing baselines, achieving a substantial reduction in pose prediction error.

Abstract

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.

Problem

• pose prediction
• resource allocation
• mmWave ISCC
• Cramer-Rao bound
• human-centric systems

Innovation

• Cramer-Rao bound
• adaptive-depth Mamba
• resource allocation
• pose prediction
• mmWave ISCC