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Secure and Robust Beamforming Design for STAR-RIS-aided MU-MIMO ISAC Systems

Rakesh Ranjan, Anshu Mukherjee, Manjesh K. Hanawal, Keshav Singh, Ioannis Krikidis

TL;DR

This paper investigates a robust, secure downlink transmission framework for a STAR-RIS empowered multi-user multiple-input multiple-output (MU-MIMO) system, where a multi-antenna dual-function radar and communication base station (DFRC-BS) that simultaneously transmits confidential messages to multiple intended users (IUs) and performs target sensing in the presence of malicious eavesdroppers is investigated.

Abstract

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) offer a transformative approach for integrated sensing and communication (ISAC) systems, particularly for enhancing physical layer security (PLS). This paper investigates a robust, secure downlink transmission framework for a STAR-RIS empowered multi-user (MU) multiple-input multiple-output (MU-MIMO) system, where a multi-antenna dual-function radar and communication base station (DFRC-BS) that simultaneously transmits confidential messages to multiple intended users (IUs) and performs target sensing in the presence of malicious eavesdroppers. To optimize system security, we formulate a worst-case robust beamforming problem to maximize the secrecy rate. This formulation jointly designs the active transmit beamforming at the BS and the passive reflection, transmission coefficients at the STAR-RIS, adheres to transmit power budgets, user quality-of-service (QoS) thresholds, sensing signal-to-interference-plus-noise ratio (SINR) requirements, maximum tolerable eavesdropping leakage, and practical phase shifts constraints. To efficiently tackle the formulated problem, we develop an alternating optimization (AO) algorithm. Specifically, the S-procedure is employed to solve semi-infinite channel uncertainty constraints, while semidefinite relaxation (SDR) and penalty convex-concave programming (CCP) are applied to obtain tractable suboptimal solutions. Extensive simulation results validate the efficacy of the proposed framework and demonstrate significant improvement in spectral efficiency compared to conventional reflecting-only RIS (R-RIS) systems under stringent sensing conditions.

Secure and Robust Beamforming Design for STAR-RIS-aided MU-MIMO ISAC Systems

TL;DR

This paper investigates a robust, secure downlink transmission framework for a STAR-RIS empowered multi-user multiple-input multiple-output (MU-MIMO) system, where a multi-antenna dual-function radar and communication base station (DFRC-BS) that simultaneously transmits confidential messages to multiple intended users (IUs) and performs target sensing in the presence of malicious eavesdroppers is investigated.

Abstract

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) offer a transformative approach for integrated sensing and communication (ISAC) systems, particularly for enhancing physical layer security (PLS). This paper investigates a robust, secure downlink transmission framework for a STAR-RIS empowered multi-user (MU) multiple-input multiple-output (MU-MIMO) system, where a multi-antenna dual-function radar and communication base station (DFRC-BS) that simultaneously transmits confidential messages to multiple intended users (IUs) and performs target sensing in the presence of malicious eavesdroppers. To optimize system security, we formulate a worst-case robust beamforming problem to maximize the secrecy rate. This formulation jointly designs the active transmit beamforming at the BS and the passive reflection, transmission coefficients at the STAR-RIS, adheres to transmit power budgets, user quality-of-service (QoS) thresholds, sensing signal-to-interference-plus-noise ratio (SINR) requirements, maximum tolerable eavesdropping leakage, and practical phase shifts constraints. To efficiently tackle the formulated problem, we develop an alternating optimization (AO) algorithm. Specifically, the S-procedure is employed to solve semi-infinite channel uncertainty constraints, while semidefinite relaxation (SDR) and penalty convex-concave programming (CCP) are applied to obtain tractable suboptimal solutions. Extensive simulation results validate the efficacy of the proposed framework and demonstrate significant improvement in spectral efficiency compared to conventional reflecting-only RIS (R-RIS) systems under stringent sensing conditions.
Paper Structure (17 sections, 1 theorem, 47 equations, 8 figures, 2 tables, 2 algorithms)

This paper contains 17 sections, 1 theorem, 47 equations, 8 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivations and Contributions
  4. Notations and Paper Outline
  5. System Model and Problem Formulation
  6. System Model
  7. Joint Communication Sensing Transmit Signal Model
  8. STAR-RIS Transmission and Reflection Model
  9. Secure Downlink Communication and Wiretap Model
  10. STAR-RIS-aided ISAC Sensing Model
  11. Formulation of the Robust Secure ISAC Problem
  12. Proposed Solution
  13. Active Beamforming Optimization at the BS
  14. STAR-RIS Phase Shift Design with Fixed BS Beamforming
  15. Computational Complexity Analysis
  16. ...and 2 more sections

Key Result

Lemma 1

Under the bounded CSI error model in (equ:G_K_channel estimation_error), the minimum QoS requirement of the IU located in the RS can be conservatively approximated by a linear lower bound evaluated at the $(q+1)$th iteration. The corresponding constraint can be expressed as: where $\bm{\hat{A}}_k,\hat{a}_k, \bm{\hat{a}}_k$ are defined in (ak), at the top of next page.

Figures (8)

  • Figure 1: Secure STAR-RIS-aided MU-MIMO ISAC systems.
  • Figure 2: Convergence behavior of the proposed optimization framework.
  • Figure 3: Achievable secrecy rate versus BS transmit power at $N=50$.
  • Figure 4: Achievable secrecy rate versus number of STAR-RIS elements $(N)$ at $P=30~\mathrm{dBm}$.
  • Figure 5: Achievable secrecy rate versus minimum required QoS of IUs $( {R_\mathrm{min}})$ at $P=30~\mathrm{dBm}$ and $N=16$.
  • ...and 3 more figures

Theorems & Definitions (1)

  • Lemma 1