Research Briefing
cs.IT 2605.29939v1 worth_reading

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

Zhonghao Liu, Yahao Ding, Yinchao Yang, Mohammad Shikh-Bahaei

Published 2026-05-28 13:51:28 相关性 1.0000 价值 0.7400 cs.IT cs.LG
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摘要

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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.

相关性判断

high
相关方向
wireless_communications integrated_sensing_communication resource_allocation information_theory_adjacent
判断依据

Strong fit for communications and adjacent information-theory review: the paper is in cs.IT, centers on indoor mmWave ISCC resource allocation, CRB-based sensing modeling, and joint sensing-computation optimization. It is not core coding theory, but it is clearly relevant to wireless communications and resource allocation.

价值判断

Strong cs.IT relevance with a clear wireless communications/resource allocation problem in indoor mmWave ISCC. Technical contribution combines CRB-guided sensing uncertainty, adaptive-depth Mamba pose inference, and joint sensing-computation allocation under practical constraints. Structure analysis indicates concrete optimization machinery, including AO, closed-form updates, and simulation comparisons against baselines.

核心问题与主要方法

核心问题

minimize short-term human pose prediction error in indoor mmWave ISCC under communication, latency, and energy constraints

场景：indoor mmWave integrated sensing, communication, and computation with joint communication, sensing, and adaptive-depth pose inference

主要方法

CRB maps sensing SNR and sensing power into range-estimation variance, which is then modeled as point-cloud perturbation affecting pose prediction. Adaptive-depth Bi-Mamba inference treats model depth L as a computation resource: each layer has an attached lightweight head so inference can stop early under latency or energy limits. An empirical MPJPE model m(p_r,L) is fitted and assumed monotone decreasing in both sensing power and inference depth, enabling resource allocation over task-level error. The optimization first reserves minimum communication power for QoS, then alternates between closed-form depth updates constrained by latency/energy and closed-form sensing-power updates constrained by energy, transmit power, and sensing detectability.

关键贡献与后续阅读

关键贡献

Introduces a CRB-guided framework that connects sensing transmit power to range-estimation uncertainty, point-cloud jitter, and downstream human pose prediction error in indoor mmWave ISCC. Uses an adaptive-depth Mamba/Bi-Mamba pose predictor with layer-wise heads so inference depth becomes an explicit optimization variable under energy and latency constraints. Formulates joint resource allocation over sensing power and model depth to minimize MPJPE while satisfying communication QoS, sensing detectability, total transmit power, latency, and energy constraints. Develops an AO-based solution that computes minimum communication power separately and uses closed-form update rules for sensing power and depth within the reduced problem. Provides simulation evidence against fixed-depth and fixed-minimum-sensing baselines, reporting sizable MPJPE improvements especially when CPU frequency or power budget permits deeper inference.

研究启发

How robust is the fitted MPJPE surface m(p_r,L) across different rooms, human motions, body poses, and mmWave propagation conditions? Are the closed-form AO updates guaranteed to converge to a stable feasible point after integer rounding of the depth variable, or only empirically observed to work? What dataset or simulator generated the point clouds and ground-truth 3D joints, and how closely does it match real indoor mmWave sensing noise? How sensitive are the gains to the assumed CRB-to-point-cloud perturbation model and to the fixed beam directions/codebook selection?

限制与不确定性

Evidence is based on structure analysis rather than full-paper validation of derivations or experimental rigor. Simulations appear limited to a single indoor scenario, so generality may be narrow. Prediction-error modeling relies on fitted empirical curves, which may weaken theoretical robustness.

原文信息

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参考文献 12
最近更新 2026-05-30 13:21
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Abstract

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CRB-Guided Framework Design and Resource Allocation for Indoor mmWave 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 Cramér–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 proble
查看参考文献
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