TY - GEN
T1 - Secure Predictive Beamforming for IRS Enabled ISAC
T2 - 2025 IEEE/CIC International Conference on Communications in China, ICCC 2025
AU - Yu, Xianglin
AU - Xu, Jinlei
AU - Dong, Chao
AU - Xing, Chengwen
AU - Zhao, Nan
AU - Wu, Qihui
AU - Niyato, Dusit
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Although integrated sensing and communication (ISAC) has an advantage of mutual gain of its dual functions, it is susceptible to be eavesdropped by mobile targets due to the broadcast nature of wireless channels. In this paper, we propose a secure predictive beamforming scheme against a mobile eavesdropping target for ISAC, where the intelligent reflecting surface (IRS) is utilized to assist the sensing and secure transmission. To tackle the eavesdropping target, we first formulate a sum secrecy rate maximization problem. However, due to the non-convex objective function and the outdated channel state information (CSI), it is difficult to solve the problem directly. Thus, we develop a deep learning based predictive beamforming scheme. It can learn the features from the historical CSI to design the beamformings for the next time slot with low computational complexity. Simulation results show that the proposed scheme can significantly enhance the security of ISAC with low overhead.
AB - Although integrated sensing and communication (ISAC) has an advantage of mutual gain of its dual functions, it is susceptible to be eavesdropped by mobile targets due to the broadcast nature of wireless channels. In this paper, we propose a secure predictive beamforming scheme against a mobile eavesdropping target for ISAC, where the intelligent reflecting surface (IRS) is utilized to assist the sensing and secure transmission. To tackle the eavesdropping target, we first formulate a sum secrecy rate maximization problem. However, due to the non-convex objective function and the outdated channel state information (CSI), it is difficult to solve the problem directly. Thus, we develop a deep learning based predictive beamforming scheme. It can learn the features from the historical CSI to design the beamformings for the next time slot with low computational complexity. Simulation results show that the proposed scheme can significantly enhance the security of ISAC with low overhead.
KW - Deep learning
KW - intelligent reflecting surface
KW - predictive beamforming
KW - secure integrated sensing and communication
UR - https://www.scopus.com/pages/publications/105017566587
U2 - 10.1109/ICCC65529.2025.11149275
DO - 10.1109/ICCC65529.2025.11149275
M3 - Conference contribution
AN - SCOPUS:105017566587
T3 - 2025 IEEE/CIC International Conference on Communications in China:Shaping the Future of Integrated Connectivity, ICCC 2025
BT - 2025 IEEE/CIC International Conference on Communications in China:Shaping the Future of Integrated Connectivity, ICCC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 August 2025 through 13 August 2025
ER -