AI-enabled STAR-RIS aided MISO ISAC Secure Communications
arxiv(2024)
摘要
A simultaneous transmitting and reflecting reconfigurable intelligent surface
(STAR-RIS) aided integrated sensing and communication (ISAC) dual-secure
communication system is studied in this paper. The sensed target and legitimate
users (LUs) are situated on the opposite sides of the STAR-RIS, and the energy
splitting and time switching protocols are applied in the STAR-RIS,
respectively. The long-term average security rate for LUs is maximized by the
joint design of the base station (BS) transmit beamforming and receive filter,
along with the STAR-RIS transmitting and reflecting coefficients, under
guarantying the echo signal-to-noise ratio thresholds and rate constraints for
the LUs. Since the channel information changes over time, conventional convex
optimization techniques cannot provide the optimal performance for the system,
and result in excessively high computational complexity in the exploration of
the long-term gains for the system. Taking continuity control decisions into
account, the deep deterministic policy gradient and soft actor-critic
algorithms based on off-policy are applied to address the complex non-convex
problem. Simulation results comprehensively evaluate the performance of the
proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is
remarkably better than the two benchmarks in the ISAC system.
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