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Secure Communication in Dynamic RDARS-Driven Systems

Ziqian Pei, Jintao Wang, Pingping Zhang, Zheng Shi, Guanghua Yang, Shaodan Ma

TL;DR

This work tackles secrecy-rate optimization in a dynamic RDARS-enabled system by formulating a joint design problem over active beamforming, reflection coefficients, and channel-aware mode selection. An alternating optimization framework leveraging penalty-based fractional programming and successive convex approximation is developed to handle the mixed-integer, non-convex constraints, with SDR/Charnes-Cooper techniques used for unit-modulus and binary variables. The proposed approach demonstrates that RDARS with channel-aware mode placement significantly improves the secrecy rate compared to conventional RIS and DAS benchmarks, and the algorithm converges reliably within a modest number of iterations. The results highlight the practical value of RDARS in enhancing physical-layer security for future wireless networks, validating the integration of mode selection as a new degree of freedom. All mathematical formulations and results are presented with explicit $${...}$$ notation to support precise replication and SEO indexing.

Abstract

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)-driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes.

Secure Communication in Dynamic RDARS-Driven Systems

TL;DR

This work tackles secrecy-rate optimization in a dynamic RDARS-enabled system by formulating a joint design problem over active beamforming, reflection coefficients, and channel-aware mode selection. An alternating optimization framework leveraging penalty-based fractional programming and successive convex approximation is developed to handle the mixed-integer, non-convex constraints, with SDR/Charnes-Cooper techniques used for unit-modulus and binary variables. The proposed approach demonstrates that RDARS with channel-aware mode placement significantly improves the secrecy rate compared to conventional RIS and DAS benchmarks, and the algorithm converges reliably within a modest number of iterations. The results highlight the practical value of RDARS in enhancing physical-layer security for future wireless networks, validating the integration of mode selection as a new degree of freedom. All mathematical formulations and results are presented with explicit notation to support precise replication and SEO indexing.

Abstract

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)-driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes.
Paper Structure (13 sections, 1 theorem, 19 equations, 3 figures, 1 algorithm)

This paper contains 13 sections, 1 theorem, 19 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Joint Beamforming and Mode Selection
  6. Mode Selection
  7. Optimizing ${\bf{S}}$ with given ${\bf{S}}_a$
  8. Optimizing ${\bf{S}}_a$ with given ${\bf{S}}$
  9. Active Beamforming
  10. Reflection Coefficients
  11. Overall Algorithm
  12. Simulation
  13. Conclusion

Key Result

Proposition 1

Problem (P2) is equivalent to where ${\bm{\beta}}\in\mathbb{C}^{N+1}$ refer to the auxiliary variable. ${\bf{U}}_{u}$ and ${\bm{\Lambda}}_{u}$ are obtained by the eigen-value decomposition (EVD) w.r.t. ${\mathbf{P}}_{u}$, i.e., ${\mathbf{P}}_{u}={\bf{U}}_{u}{\bm{\Lambda}}_{u}{\bf{U}}_{u}^H$.

Figures (3)

  • Figure 1: A RDARS-aided secure transmission system.
  • Figure 2: Convergence behaviors of the proposed algorithm.
  • Figure 3: Comparison of secrecy rate versus system parameters (a) Transmit Power $P$ (b) Number of Elements in RDARS $N$ (c) Number of Elements in connection mode $a$.

Theorems & Definitions (2)

  • Proposition 1
  • proof