通过手势识别动态调整太赫兹通信资源，提升室内感知与通信性能。
Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation
- 基于扩展卡尔曼滤波追踪手势，动态优化资源分配。
- 相比传统方法，感知精度和通信性能显著提升。
- 适合需要手势交互的智能室内场景应用。
本文研究了一种工作于太赫兹频段的多用户室内融合感知与通信（ISAC）系统，旨在根据手势识别结果实现自适应通信。通过扩展卡尔曼滤波（EKF）进行手势跟踪，接入点（AP）能够根据检测到的手势变化动态调整资源分配，从而提升感知精度。基于手势识别结果，AP进一步更新不同用户的通信质量要求，实现高效资源分配。为此，提出一种自适应联合优化算法，用于功率分配与波束成形，以最大化整体感知信干噪比（SINR），同时满足依赖手势的通信服务质量（QoS）约束。仿真结果表明，所提方法能有效响应手势动态变化，在感知精度和通信性能上均优于传统单变量优化基线。
原文摘要 · Abstract (English)
This paper investigates a multi-user indoor integrated sensing and communication (ISAC) system operating in the terahertz (THz) band, designed for adaptive communication based on gesture recognition. Leveraging gesture tracking through an extended Kalman filter (EKF), the access point (AP) dynamically adjusts resource allocation in response to detected gesture variations, thereby improving sensing accuracy. Based on the gesture recognition results, the AP further updates the communication quality requirements of different users, enabling efficient resource allocation. To this end, an adaptive joint optimization algorithm for power allocation and beamforming is developed to maximize the overall sensing signal-to-interference-plus-noise ratio (SINR) while satisfying the gesture-dependent communication quality of service (QoS) constraints. Simulation results demonstrate that the proposed method effectively responds to gesture dynamics, achieving superior sensing accuracy and communication performance compared with conventional single-variable optimization baselines.
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