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This paper proposes a THz band ISAC system for indoor environments that dynamically adapts communication resource allocation based on gesture recognition. An extended Kalman filter tracks gestures, which then informs an adaptive joint optimization algorithm for power allocation and beamforming. Results show the proposed method improves both sensing accuracy (SINR) and communication performance (QoS) compared to static allocation strategies.
Unlock better sensing and communication in THz systems by dynamically adapting resource allocation based on real-time gesture recognition.
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.