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Related Experiment Videos

RIS-Aided Physical Layer Security with Imperfect CSI: A Robust Model-Driven Deep Learning Approach.

Ruikai Miao1,2, Zhiqun Song1,2, Yong Li1,2

  • 1The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

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This summary is machine-generated.

This study enhances physical layer security using reconfigurable intelligent surfaces (RIS) despite imperfect channel information. A novel deep learning approach improves robustness and maximizes secrecy rates for secure wireless communication.

Area of Science:

  • Wireless Communication
  • Information Security
  • Artificial Intelligence

Background:

  • Reconfigurable intelligent surfaces (RIS) offer new physical layer security possibilities.
  • Imperfect eavesdropper channel state information (CSI) is a significant challenge in RIS-aided security.
  • Existing methods struggle with robustness against CSI uncertainties.

Purpose of the Study:

  • To enhance RIS-aided physical layer security under imperfect eavesdropper CSI.
  • To formulate and solve a robust weighted sum secrecy rate maximization problem.
  • To develop an efficient model-driven deep learning solution.

Main Methods:

  • Formulated a robust weighted sum secrecy rate maximization problem.
  • Proposed a model-driven deep learning approach based on gradient descent-ascent.
Keywords:
CSIdeep unfold networkmodel-drivenphysical layer securityreconfigurable intelligent surface (RIS)

Related Experiment Videos

  • Unfolded the algorithm into a gated recurrent unit (GRU)-aided deep unfold network.
  • Enabled adaptive iteration adjustment using GRU's sequential learning capability.

    • Main Results:

    • The proposed GRU-aided deep unfold network demonstrated robustness against imperfect CSI.
    • Achieved higher weighted sum secrecy rates compared to existing methods.
    • Outperformed traditional deep unfold networks with fixed iterations.

    Conclusions:

    • The developed deep learning approach effectively addresses the challenge of imperfect CSI in RIS-aided security.
    • The method provides a robust and efficient solution for maximizing physical layer security.
    • This work advances secure communication strategies in the presence of channel uncertainties.