TY - GEN
T1 - UAV-IRS-Assisted ISAC Secure Transmission Design
AU - Chen, Yilin
AU - Gong, Yanyun
AU - Sun, Wenbin
AU - Liu, Haochen
AU - Wang, Ling
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Integrated Sensing and Communication (ISAC), as a core pillar of 6G, provides critical enablement for innovative applications across industries by sharing hardware and spectrum resources. However, in complex and dynamic environments, blockage and malicious eavesdropping pose serious challenges to secure transmission and accurate sensing. In this paper, an intelligent reflecting surface (IRS) aided ISAC system for physical-layer security (PLS) is considered, in which a co-design of base station (BS) beamforming and IRS phase shift matrix is proposed to enhance the security performance. The optimization seeks to maximize the long-term average secrecy rate of legitimate users (LUs), while a minimum echo SNR and the BS transmit power limitation are imposed as constraints. To address the inherent nonconvexity of the optimization, we employ two deep reinforcement learning (DRL) algorithms. Simulation validated the advantages of the two DRL approaches in terms of efficiency and scalability. The introduction of IRS within the ISAC system delivers significant performance gains, further demonstrating the promising future of IRS-enabled ISACs in 6G.
AB - Integrated Sensing and Communication (ISAC), as a core pillar of 6G, provides critical enablement for innovative applications across industries by sharing hardware and spectrum resources. However, in complex and dynamic environments, blockage and malicious eavesdropping pose serious challenges to secure transmission and accurate sensing. In this paper, an intelligent reflecting surface (IRS) aided ISAC system for physical-layer security (PLS) is considered, in which a co-design of base station (BS) beamforming and IRS phase shift matrix is proposed to enhance the security performance. The optimization seeks to maximize the long-term average secrecy rate of legitimate users (LUs), while a minimum echo SNR and the BS transmit power limitation are imposed as constraints. To address the inherent nonconvexity of the optimization, we employ two deep reinforcement learning (DRL) algorithms. Simulation validated the advantages of the two DRL approaches in terms of efficiency and scalability. The introduction of IRS within the ISAC system delivers significant performance gains, further demonstrating the promising future of IRS-enabled ISACs in 6G.
KW - deep reinforcement learning (DRL)
KW - integrated sensing and communication (ISAC)
KW - intelligent reflecting surface (IRS)
KW - secure communication
UR - https://www.scopus.com/pages/publications/105042576196
U2 - 10.1007/978-981-95-8232-7_69
DO - 10.1007/978-981-95-8232-7_69
M3 - 会议稿件
AN - SCOPUS:105042576196
SN - 9789819582310
T3 - Lecture Notes in Electrical Engineering
SP - 731
EP - 744
BT - Proceedings of the 4th International Conference on Sensing, Measurement, Communication and Internet of Things Technologies
A2 - Zhao, Zhenyu
A2 - Jin, Peiquan
A2 - Zhang, Mingchuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2025
Y2 - 28 November 2025 through 30 November 2025
ER -