Research Briefing
cs.IT 2605.29913v1 worth_reading

Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation

Zhonghao Liu, Yinchao Yang, Yahao Ding, Yixuan Wang, Mohammad Shikh-Bahaei

Published 2026-05-28 13:29:30 相关性 1.0000 价值 0.7400 cs.IT cs.LG
arXiv PDF HTML

摘要

选中正文可添加批注

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.

相关性判断

high
相关方向
wireless_communications integrated_sensing_and_communication resource_allocation beamforming thz_systems
判断依据

Directly about THz integrated sensing and communication with beamforming, power allocation, QoS constraints, and adaptive resource optimization, which is squarely adjacent to information theory and communications.

价值判断

Highly relevant to cs.IT communications, combining THz ISAC, gesture-aware QoS adaptation, beamforming, and power allocation. Structure evidence indicates a concrete optimization formulation with EKF tracking, fractional programming, alternating optimization, and SDR. Worth reading because it has clear technical substance and application novelty, but evidence points to simulation-only validation rather than broader theoretical or experimental impact.

核心问题与主要方法

核心问题

Adaptive resource allocation in multi-user indoor THz integrated sensing and communication under time-varying gesture-driven communication demands

场景：Indoor THz ISAC system with one AP, K single-antenna users, ULA at the AP, LoS channel model, and gesture-induced state transitions between device pickup and putdown

主要方法

Echo measurements are converted into delay, Doppler, distance, and AoA estimates, then tracked by an EKF over a per-user [distance, AoA] state. Gesture recognition uses predicted height variation h_{l,k}=d_{l,k} cos(theta_{l,k}) and a threshold to classify pickup, putdown, or inactive state. Recognized gesture state controls a binary QoS indicator that switches each user's required communication SINR between high and low thresholds. Resource allocation maximizes sensing SINR while preserving communication QoS through joint optimization of transmit powers and communication/sensing beamformers. Fractional programming, quadratic transform, alternating optimization, and SDR decompose the original nonconvex problem into iteratively solved convex subproblems.

关键贡献与后续阅读

关键贡献

Formulates a gesture-aware multi-user indoor THz ISAC resource-allocation problem where user communication QoS thresholds are dynamically inferred from recognized pickup/putdown gestures. Introduces an EKF-based gesture state prediction and tracking module using echo-derived delay, Doppler, distance, and AoA information to support real-time resource management. Defines a gesture-to-QoS mapping in which a binary per-user state controls high or low communication SINR requirements. Develops a joint power allocation and beamforming optimization that maximizes sum sensing SINR under total power, unit-norm beamforming, and gesture-dependent communication SINR constraints. Applies fractional programming with quadratic transform, alternating optimization, and semidefinite relaxation to obtain tractable convex beamforming and power-allocation subproblems. Provides simulation comparisons against single-variable optimization baselines and a static non-adaptive baseline under static and dynamic gesture scenarios.

研究启发

How sensitive are the results to the gesture-height threshold epsilon_h and EKF noise covariance choices? Does the SDR step reliably recover rank-one beamforming vectors, or is randomization/approximation needed in some regimes? How strong are the baselines beyond power-only, beam-only, and static adaptation, especially against recent adaptive ISAC resource-allocation methods? What happens when the LoS assumption, stationary-user assumption, or M >> K spatial-separability assumption is relaxed?

限制与不确定性

Impact may be limited by restrictive assumptions: LoS-dominant channel, stationary users, fixed height, and hand gestures only. Novelty could be incremental if the main contribution is combining established EKF tracking and standard ISAC resource allocation tools. No full-paper review was performed, so baseline strength and mathematical rigor are inferred from structure analysis only.

原文信息

正文记录 1
参考文献 15
最近更新 2026-05-30 13:21
查看正文预览 
Abstract

Content selection saved. Describe the issue below:

Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation

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 optimizati
查看参考文献
  1. [1] T. Bacchielli, L. Pucci, E. Favarelli, D. Dardari, and A. Giorgetti Bistatic thz sensing with mimo-ofdm: a two-stage transmission approach with maximum likelihood estimation . Authorea Preprints . Cited by: §I .
  2. [2] Z. Fang, Y. Lyu, Z. Yu, and C. Han (2025) Environment reconstruction in terahertz monostatic sensing: joint millimeter-level geometry mapping and material identification . IEEE Journal of Selected Topics in Electromagnetics, Antennas and Propagation . Cited by: §I .
  3. [3] H. Hua, J. Xu, and T. X. Han (2023) Optimal transmit beamforming for integrated sensing and communication . IEEE Transactions on Vehicular Technology 72 ( 8 ), pp. 10588–10603 . External Links: Document Cited by: § V-A .
  4. [4] L. Li, W. Chen, Z. Chen, T. Hu, W. Mei, and B. Ning (2024) Enhancing terahertz communications coverage with isac-assisted beam management . IEEE Wireless Communications 31 ( 1 ), pp. 34–40 . External Links: Document Cited by: §I .
  5. [5] B. Liao, H. Q. Ngo, M. Matthaiou, and P. J. Smith (2024) Power allocation for massive mimo-isac systems . IEEE Transactions on Wireless Communications 23 ( 10 ), pp. 14232–14248 . External Links: Document Cited by: § V-A .
  6. [6] R. Liu, M. Li, Q. Liu, and A. L. Swindlehurst (2023) SNR/crb-constrained joint beamforming and reflection designs for ris-isac systems . IEEE Transactions on Wireless Communications 23 ( 7 ), pp. 7456–7470 . Cited by: § II-C .
  7. [7] Y. Pan, K. Wang, C. Pan, H. Zhu, and J. Wang (2022) Sum-rate maximization for intelligent reflecting surface assisted terahertz communications . IEEE Transactions on Vehicular Technology 71 ( 3 ), pp. 3320–3325 . External Links: Document Cited by: §V .
  8. [8] A. Shafie, N. Yang, S. A. Alvi, C. Han, S. Durrani, and J. M. Jornet (2022) Spectrum allocation with adaptive sub-band bandwidth for terahertz communication systems . IEEE Transactions on Communications 70 ( 2 ), pp. 1407–1422 . External Links: Document Cited by: § II-B .
  9. [9] W. Shang, Y. Liao, V. Friderikos, and H. Yanikomeroglu (2024) Unlocking sub-thz by robotic aerial base stations: joint deployment and wireless backhaul routing . IEEE Open Journal of the Communications Society . Cited by: §I .
  10. [10] D. Simon (2006) Optimal state estimation: kalman, h infinity, and nonlinear approaches . Wiley . Cited by: § III-B .
  11. [11] V. Towhidlou and M. Shikh-Bahaei (2018) Adaptive full-duplex communications in cognitive radio networks . IEEE Transactions on Vehicular Technology 67 ( 9 ), pp. 8386–8395 . Cited by: § II-A .
  12. [12] Q. Wu, Y. Zhang, Z. Yang, and M. R. Shikh-Bahaei (2024) Deep channel prediction-based energy-efficient intelligent reflecting surface-aided terahertz communications . IEEE Transactions on Wireless Communications 23 ( 4 ), pp. 2946–2960 . External Links: Document Cited by: § II-B .
  13. [13] Y. Yang and M. Shikh-Bahaei (2023) Secure integrated sensing and communication for conventional and isac-dedicated receivers . In 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) , Vol. , pp. 1–6 . External Links: Document Cited by: §I .
  14. [14] H. Zhang, T. Yang, X. Wu, Z. Guo, and B. Hu (2023) Robust beamforming design for uav communications based on integrated sensing and communication . EURASIP Journal on Wireless Communications and Networking 2023 ( 1 ), pp. 88 . Cited by: § III-A .
  15. [15] J. Zhou, Y. Yang, Z. Yang, and M. R. Shikh-Bahaei (2024) Near-field extremely large-scale star-ris enabled integrated sensing and communications . IEEE Transactions on Green Communications and Networking 9 ( 1 ), pp. 404–416 . Cited by: §I .