Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation
For indoor THz ISAC systems, this work introduces gesture recognition to dynamically adjust resource allocation, addressing the need for adaptive sensing and communication in human-centric environments.
This paper proposes a gesture-aware indoor THz ISAC system that uses an extended Kalman filter for gesture tracking to adaptively allocate resources, achieving improved sensing accuracy and communication performance over conventional baselines.
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.