arXiv:2605.29939v1 Announce Type: cross 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
The increasing demand for precise indoor human-centric applications, coupled with advancements in millimeter-wave (mmWave) technology and AI, makes this research timely.
This development could significantly enhance the capabilities of integrated sensing, communication, and computation (ISCC) systems, crucial for next-generation indoor AI applications and robotics.
The proposed framework minimizes human pose prediction error, improving the reliability and efficiency of indoor ISCC systems under real-world constraints.
- · AI hardware manufacturers
- · Robotics companies
- · Smart building developers
- · Telecommunications providers
- · Legacy tracking technologies
- · Systems with high latency needs
More accurate and faster indoor human tracking and interaction will become possible, enhancing user experience and operational efficiency.
This could accelerate the adoption of advanced robotics and AI agents in indoor environments for tasks requiring precise human interaction and real-time computation.
The enhanced efficiency and accuracy may lead to new indoor navigation, safety, and productivity applications not currently feasible.
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Read at arXiv cs.LG