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Encoded Jamming Secure Communication for RIS-Assisted and ISAC Systems

Hao Yang, Hao Xu, Kai Wan, Sijie Zhao, Robert Caiming Qiu

TL;DR

This work tackles secure wireless communications by marrying encoded jamming (EJ) with reconfigurable intelligent surfaces (RIS). It develops a joint optimization framework that leverages SDR/MM for MISO EJ design and a low-complexity EJ–WMMSE algorithm for MIMO EJ, and further extends EJ to integrated sensing and communications (ISAC) to derive a Pareto frontier between secrecy rate and sensing mutual information. The results show that RIS-enhanced EJ can significantly outperform traditional GN-based approaches across various channel conditions, and that the ISAC extension supports effective trade-offs between security and sensing performance. Together, these contributions propose a practical, scalable path toward robust physical-layer security in next-generation RIS-enabled networks and ISAC scenarios, with future work on robustness to CSI imperfections and discrete RIS phase profiles.

Abstract

This paper considers a cooperative jamming (CJ)-aided secure wireless communication system. Conventionally, the jammer transmits Gaussian noise (GN) to enhance security; however, the GN scheme also degrades the legitimate receiver's performance. Encoded jamming (EJ) mitigates this interference but does not always outperform GN under varying channel conditions. To address this limitation, we propose a joint optimization framework that integrates reconfigurable intelligent surface (RIS) with EJ to maximize the secrecy rate. In the multiple-input single-output (MISO) case, we adopt a semidefinite relaxation (SDR)-based alternating optimization method, while in the multiple-input multiple-output (MIMO) case, we develop an alternating optimization algorithm based on the weighted sum mean-square-error minimization (WMMSE) scheme. Furthermore, we are the first to incorporate EJ into an integrated sensing and communication (ISAC) system, characterizing the Pareto boundary between secrecy rate and sensing mutual information (MI) by solving the resulting joint optimization problem using a modified WMMSE-based algorithm. Simulation results show that the proposed schemes significantly outperform benchmark methods in secrecy rate across diverse channel conditions and clearly reveal the trade-off between security and sensing.

Encoded Jamming Secure Communication for RIS-Assisted and ISAC Systems

TL;DR

This work tackles secure wireless communications by marrying encoded jamming (EJ) with reconfigurable intelligent surfaces (RIS). It develops a joint optimization framework that leverages SDR/MM for MISO EJ design and a low-complexity EJ–WMMSE algorithm for MIMO EJ, and further extends EJ to integrated sensing and communications (ISAC) to derive a Pareto frontier between secrecy rate and sensing mutual information. The results show that RIS-enhanced EJ can significantly outperform traditional GN-based approaches across various channel conditions, and that the ISAC extension supports effective trade-offs between security and sensing performance. Together, these contributions propose a practical, scalable path toward robust physical-layer security in next-generation RIS-enabled networks and ISAC scenarios, with future work on robustness to CSI imperfections and discrete RIS phase profiles.

Abstract

This paper considers a cooperative jamming (CJ)-aided secure wireless communication system. Conventionally, the jammer transmits Gaussian noise (GN) to enhance security; however, the GN scheme also degrades the legitimate receiver's performance. Encoded jamming (EJ) mitigates this interference but does not always outperform GN under varying channel conditions. To address this limitation, we propose a joint optimization framework that integrates reconfigurable intelligent surface (RIS) with EJ to maximize the secrecy rate. In the multiple-input single-output (MISO) case, we adopt a semidefinite relaxation (SDR)-based alternating optimization method, while in the multiple-input multiple-output (MIMO) case, we develop an alternating optimization algorithm based on the weighted sum mean-square-error minimization (WMMSE) scheme. Furthermore, we are the first to incorporate EJ into an integrated sensing and communication (ISAC) system, characterizing the Pareto boundary between secrecy rate and sensing mutual information (MI) by solving the resulting joint optimization problem using a modified WMMSE-based algorithm. Simulation results show that the proposed schemes significantly outperform benchmark methods in secrecy rate across diverse channel conditions and clearly reveal the trade-off between security and sensing.

Paper Structure

This paper contains 38 sections, 2 theorems, 102 equations, 9 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Overview of RIS-assisted communication systems
  3. Main contribution
  4. Preliminaries
  5. Traditional CJ Technique based on GN
  6. Modeling of the Traditional CJ System
  7. Coding-Enhanced CJ Technique
  8. A Key Lemma for the WMMSE Extension
  9. System Model And Problem Formulation
  10. System Model
  11. Formulation of Optimization Problems
  12. Coding-Enhanced cooperative jamming Design for RIS-Assisted Secure Communications
  13. MISO Case
  14. EJ Scheme
  15. Optimizing $\mathbf{f}_1$ and $\mathbf{f}_2$ for a given $\overline{\mathbf{w}}$
  16. ...and 23 more sections

Key Result

Lemma 1

Define an $q$ by $q$ matrix function where $\mathbf{U},\mathbf{H}\in\mathbb{C}^{p\times q}$, $\mathbf{F},\mathbf{R}\in\mathbb{C}^{q\times q}$, $\mathbf{N}\in\mathbb{C}^{p\times p}$, and ${\mathbf R},{\mathbf N}$ are positive definite matrices. The following three facts hold.

Figures (9)

  • Figure 1: Traditional wireless network with a cooperative jammer.
  • Figure 2: An RIS-assisted wireless network with a cooperative jammer.
  • Figure 3: Secure ISAC system with a cooperative jammer.
  • Figure 4: MISO case: average secrecy rate obtained by different schemes versus $P$ with $N_{\text{u}}=N_{\text{e}}=1$.
  • Figure 5: MIMO case without RIS: average secrecy rate obtained by different schemes versus $N_{\text{u}}$ with $N_{\text{b}}=2, N_{\text{c}}=N_{\text{e}}=4$.
  • ...and 4 more figures

Theorems & Definitions (7)

  • Remark 1
  • Lemma 1
  • Remark 2
  • Remark 3
  • Theorem 1
  • proof
  • proof