发表机构
King’s College London; Zhejiang University（伦敦国王学院; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对ISCC系统，提出CRB引导的感知框架与交替优化算法，联合优化波束成形矩阵等，实现自适应深度姿态预测，在资源约束下将预测性能提升最多35%。
AI 中文摘要
感知、通信与计算一体化（ISCC）为室内以人为中心的应用提供了极具前景的框架，在这些应用中，短期人体姿态预测可助力实现连续的人体姿态跟踪与主动的资源分配。本文提出一种基于克拉美罗界（CRB）的引导感知框架，研究资源受限ISCC系统中最小化预测误差的问题。具体而言，首先开发姿态预测模型ET-Mamba，用于预测人体关节位置以实现连续跟踪；为应对计算资源限制，在不同推理层附加轻量级预测头，实现自适应深度的姿态预测。随后引入CRB引导的扰动策略，将不同感知信噪比（SNR）水平下的感知不确定性转化为点云扰动，基于此建立姿态预测误差、感知SNR与模型推理深度之间的经验关系。此外，为在有限资源下提升预测精度，本文构建资源分配优化问题，通过联合优化波束成形矩阵、模型推理深度与计算频率，最小化姿态预测误差；为求解该混合整数非凸优化问题，提出基于交替优化（AO）的算法，将闭式更新与半定规划（SDP）集成到迭代求解过程中。仿真结果表明，所提方法在资源约束下可将姿态预测性能提升最多35%，验证了在ISCC系统中开展感知、通信与计算联合设计的有效性。
英文摘要
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 pose tracking and proactive resource allocation. This paper proposes a Cramer-Rao bound (CRB)-guided sensing framework and investigates a problem of minimizing prediction error in resource-constrained ISCC systems. Specifically, a pose prediction model (ET-Mamba) is first developed to predict human joint positions for continuous tracking. To account for computation-resource limitations, lightweight prediction heads are attached to different inference layers, enabling adaptive-depth pose prediction. A CRB-guided perturbation strategy is then introduced to translate sensing uncertainty at different sensing SNR levels into point-cloud perturbations. Based on that, an empirical relationship among pose prediction error, sensing SNR, and model inference depth is established. Furthermore, to improve prediction accuracy under limited resources, this paper formulates a resource allocation optimization problem that minimizes the pose prediction error by jointly optimizing the beamforming matrix, model inference depth, and computation frequency. To solve this mixed-integer non-convex optimization problem, we propose an alternating optimization (AO)-based algorithm, where closed-form updates and semidefinite programming (SDP) are integrated into the iterative solution process. Simulation results show that the proposed method effectively improves pose prediction performance by up to 35 percent under resource constraints, verifying the effectiveness of conducting joint sensing, communication, and computation design in ISCC systems.
