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

arXiv: https://arxiv.org/abs/2605.29939 · HTML: https://mlsanity.com/paper/2605.29939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 6 of 6)

- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems](https://mlsanity.com/paper/2301.11134/) (2023)
- [Diffusion Model-Enhanced Environment Reconstruction in ISAC](https://mlsanity.com/paper/2511.19044/) (2025)
- [Fundamental CRB-Rate Tradeoff in Multi-antenna Multicast Channel with ISAC](https://mlsanity.com/paper/2205.15615/) (2022)
- [Sensing Framework Design and Performance Optimization with Action Detection for ISCC](https://mlsanity.com/paper/2505.02554/) (2025)

## Abstract (arXiv)

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.