Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Secrecy Rate Maximization in RIS-Assisted MIMO Systems Using a Practical Hardware Model

Rakesh Ranjan, Ahmad Sirojuddin, Manjesh K. Hanawal, Himanshu B. Mishra, Wan-Jen Huang ID

TL;DR

Simulation results confirm the effectiveness of the proposed PGM and demonstrate that adopting a practical RIS model is essential for establishing secure RIS-assisted MIMO communication links, especially under varying RE resistance values.

Abstract

This study investigates a robust reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) system for secure wireless communication, in which a multi-antenna transmitter (Alice) sends confidential messages to a multi-antenna receiver (Bob) in the presence of an eavesdropper (Eve). Unlike idealized models, the reflecting elements (REs) of the RIS are assumed to possess inherent electrical resistance, introducing a practical non-ideal effect often neglected in prior research. The aim of the study is to maximize the secrecy rate of the MIMO system under perfect knowledge of the channel state information (CSI). To achieve this, the secrecy rate maximization problem is formulated and solved using a low-complexity joint optimization framework based on an adaptive projected gradient method (PGM), which simultaneously updates both the transmit precoding matrix and the RIS phase shifts. Solving the exact problem is computationally complex. Thus, a simplified variant is further introduced that maximizes the channel power difference rather than the exact secrecy rate. The simulation results show that this approximation yields a secrecy rate close to the true optimum while significantly reducing the computational cost. In addition, the proposed PGM with an adaptive step size initialization and control mechanism substantially improves the secrecy rate and reduces the computational time compared to the conventional fixed step size PGM. Overall, the simulation results confirm the effectiveness of the proposed PGM and demonstrate that adopting a practical RIS model is essential for establishing secure RIS-assisted MIMO communication links, especially under varying RE resistance values.

Secrecy Rate Maximization in RIS-Assisted MIMO Systems Using a Practical Hardware Model

TL;DR

Simulation results confirm the effectiveness of the proposed PGM and demonstrate that adopting a practical RIS model is essential for establishing secure RIS-assisted MIMO communication links, especially under varying RE resistance values.

Abstract

This study investigates a robust reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) system for secure wireless communication, in which a multi-antenna transmitter (Alice) sends confidential messages to a multi-antenna receiver (Bob) in the presence of an eavesdropper (Eve). Unlike idealized models, the reflecting elements (REs) of the RIS are assumed to possess inherent electrical resistance, introducing a practical non-ideal effect often neglected in prior research. The aim of the study is to maximize the secrecy rate of the MIMO system under perfect knowledge of the channel state information (CSI). To achieve this, the secrecy rate maximization problem is formulated and solved using a low-complexity joint optimization framework based on an adaptive projected gradient method (PGM), which simultaneously updates both the transmit precoding matrix and the RIS phase shifts. Solving the exact problem is computationally complex. Thus, a simplified variant is further introduced that maximizes the channel power difference rather than the exact secrecy rate. The simulation results show that this approximation yields a secrecy rate close to the true optimum while significantly reducing the computational cost. In addition, the proposed PGM with an adaptive step size initialization and control mechanism substantially improves the secrecy rate and reduces the computational time compared to the conventional fixed step size PGM. Overall, the simulation results confirm the effectiveness of the proposed PGM and demonstrate that adopting a practical RIS model is essential for establishing secure RIS-assisted MIMO communication links, especially under varying RE resistance values.
Paper Structure (20 sections, 1 theorem, 34 equations, 8 figures, 6 tables, 2 algorithms)

This paper contains 20 sections, 1 theorem, 34 equations, 8 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Motivations and Contributions
  4. Notations and Organization of Paper
  5. System Model and Problem Formulation
  6. RIS-Assisted MIMO Systems
  7. Practical RIS Phase Shift Model
  8. Problem Formulation
  9. Joint Precoding Matrix and Phase Shift Optimization
  10. Sub-optimal Phase Shift with CPDM
  11. Numerical Results and Discussion
  12. Benchmark Schemes
  13. Secrecy Rate Versus BS Transmit Power
  14. Secrecy Rate Versus Number of RIS elements
  15. Run Time Versus Number of RIS Elements
  16. ...and 5 more sections

Key Result

Proposition 1

The CPDM criterion expressed in (prob:CPDM:obj) is a surrogate function of the secrecy rate criterion in (prob:main:obj) and serves as an upper bound when $\mathbf{F}_{\text{b}} - \mathbf{F}_{\text{e}} \succeq \mathbf{0}$, where $\mathbf{F}_{\text{b}} \triangleq \mathbf{T}^H \hat{\mathbf{H}}_{\text{

Figures (8)

  • Figure 1: System model of RIS-assisted MIMO systems.
  • Figure 2: Relationship between $\beta_m$ and $\theta_m$ in practical RIS system as modeled by (\ref{['eq:practical model3']}) for different values of $R$.
  • Figure 3: Illustration of precoder $\mathbf{T}$ projection. Given $\mathbf{T}^{(i_1-1)}$, $\hat{\mathbf{T}}$ is obtained by shifting $\mathbf{T}^{(1_1-1)}$ toward the direction of $\partial C_{\text{sec}} / \partial \mathbf{T}^*$. Then, $\mathbf{T}^{(i_1)}$ is obtained by projecting $\hat{\mathbf{T}}$ to a set that satisfies (\ref{['prob:main:const power']}).
  • Figure 4: Illustration of the angle projection. $\theta_m^{(i)}$ is set to be $\theta_m^{(i)} = \hat{\theta}_m$, $\theta_m^{(i)} = \theta_{\text{min}}$ and $\theta_m^{(i)} = \theta_{\text{max}}$ when $\hat{\theta}_m$ is in Area 1, Area 2, and Area 3, respectively, as explained in (\ref{['eq:theta m project']}).
  • Figure 5: Position of blocks.
  • ...and 3 more figures

Theorems & Definitions (1)

  • Proposition 1