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Reinforcement Learning-Based Secure Near-field Directional Modulation Enhanced by Rotatable RIS

Yongqiang Li, Feng Shu, Shaofan Chen, Yuanyuan Wu, Maolin Li, Zhen Chen, Hao Jiang, Jiangzhou Wang

Abstract

This paper investigates secure Directional Modulation (DM) design enhanced by a rotatable active Reconfigurable Intelligent Surface (RIS). In conventional RIS-assisted DM networks, the security performance gain is limited due to the multiplicative path loss introduced by the RIS reflection path. To address this challenge, a Secrecy Rate (SR) maximization problem is formulated, subject to constraints including the eavesdropper's Direction Of Arrival (DOA) estimation performance, transmit power, rotatable range, and maximum reflection amplitude of the RIS elements. To solve this non-convex optimization problem, three algorithms are proposed: a multi-stream null-space projection and leakage-based method, an enhanced leakage-based method, and an optimization scheme based on the Distributed Soft Actor-Critic with Three refinements (DSAC-T). Simulation results validate the effectiveness of the proposed algorithms. A performance trade-off is observed between eavesdropper's DOA estimation accuracy and the achievable SR. The security enhancement provided by the RIS is more significant in systems equipped with a small number of antennas. By optimizing the orientation of the RIS, a 52.6\% improvement in SR performance can be achieved.

Reinforcement Learning-Based Secure Near-field Directional Modulation Enhanced by Rotatable RIS

Abstract

This paper investigates secure Directional Modulation (DM) design enhanced by a rotatable active Reconfigurable Intelligent Surface (RIS). In conventional RIS-assisted DM networks, the security performance gain is limited due to the multiplicative path loss introduced by the RIS reflection path. To address this challenge, a Secrecy Rate (SR) maximization problem is formulated, subject to constraints including the eavesdropper's Direction Of Arrival (DOA) estimation performance, transmit power, rotatable range, and maximum reflection amplitude of the RIS elements. To solve this non-convex optimization problem, three algorithms are proposed: a multi-stream null-space projection and leakage-based method, an enhanced leakage-based method, and an optimization scheme based on the Distributed Soft Actor-Critic with Three refinements (DSAC-T). Simulation results validate the effectiveness of the proposed algorithms. A performance trade-off is observed between eavesdropper's DOA estimation accuracy and the achievable SR. The security enhancement provided by the RIS is more significant in systems equipped with a small number of antennas. By optimizing the orientation of the RIS, a 52.6\% improvement in SR performance can be achieved.
Paper Structure (19 sections, 64 equations, 12 figures, 1 table, 2 algorithms)

This paper contains 19 sections, 64 equations, 12 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Motivations and Contributions
  4. Organization and Notation
  5. System model and problem formulation
  6. System model
  7. Channel model
  8. Problem formulation
  9. Stage 1: DOA estimation of Eve and RIS orientation initialization
  10. Stage 2: SR maximization
  11. Proposed optimization algorithms
  12. Proposed MNPL method
  13. Proposed EL method
  14. Proposed Optimization Scheme Based on DSAC-T
  15. Critic update
  16. ...and 4 more sections

Figures (12)

  • Figure 1: Rotatable active RIS-enhanced secure communication model.
  • Figure 2: Optimization scheme based on DSAC-T.
  • Figure 3: Reward versus training episode.
  • Figure 4: SR under different azimuth angles and distances from the RIS. (a) Data stream 1 under Algorithm 1. (b) Data stream 2 under Algorithm 1. (c) Data stream 1 under Algorithm 2. (d) Data stream 2 under Algorithm 2.
  • Figure 5: CRLB under different azimuth and elevation angles for RIS. Data stream 2 corresponding to Eve's azimuth angle.
  • ...and 7 more figures