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.

翻译：本文研究了一种工作在太赫兹（THz）频段的多用户室内集成感知与通信（ISAC）系统，旨在基于手势识别实现自适应通信。通过利用扩展卡尔曼滤波（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服务质量要求，实现高效的资源分配。为此，本文提出了一种功率分配与波束成形的自适应联合优化算法，在满足与手势相关的通信服务质量（QoS）约束的前提下，最大化整体感知信干噪比（SINR）。仿真结果表明，与传统单变量优化基线相比，所提方法能够有效响应手势动态，实现更优的感知精度和通信性能。