Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces (1912.01497v3)

Abstract: In this paper, intelligent reflecting surfaces (IRSs) are employed to enhance the physical layer security in a challenging radio environment. In particular, a multi-antenna access point (AP) has to serve multiple single-antenna legitimate users, which do not have line-of-sight communication links, in the presence of multiple multi-antenna potential eavesdroppers whose channel state information (CSI) is not perfectly known. Artificial noise (AN) is transmitted from the AP to deliberately impair the eavesdropping channels for security provisioning. We investigate the joint design of the beamformers and AN covariance matrix at the AP and the phase shifters at the IRSs for maximization of the system sum-rate while limiting the maximum information leakage to the potential eavesdroppers. To this end, we formulate a robust nonconvex optimization problem taking into account the impact of the imperfect CSI of the eavesdropping channels. To address the non-convexity of the optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization, a penalty-based approach, successive convex approximation, and semidefinite relaxation. Simulation results show that IRSs can significantly improve the system secrecy performance compared to conventional architectures without IRS. Furthermore, our results unveil that, for physical layer security, uniformly distributing the reflecting elements among multiple IRSs is preferable over deploying them at a single IRS.

• The paper introduces an IRS-assisted MISO system that leverages beamforming and artificial noise to secure data transmission against eavesdropping threats.
• It formulates a non-convex optimization problem addressing CSI uncertainty and IRS phase shift constraints using iterative methods like SDR and SCA.
• Numerical results demonstrate that distributing IRS elements across multiple surfaces substantially improves physical layer security compared to a single IRS setup.

Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces

This paper addresses a critical challenge in contemporary wireless communication: securing multiuser multiple-input single-output (MISO) systems in the presence of potential eavesdroppers. The paper leverages intelligent reflecting surfaces (IRSs) to enhance physical layer security, particularly when direct line-of-sight (LoS) communication is obstructed.

Key Contributions

1. System Model and Design: The researchers propose a system wherein a multi-antenna access point (AP) serves multiple single-antenna legitimate users via IRSs. These IRSs mediate communication by altering the propagation environment to favor legitimate users, thereby minimizing the risk of eavesdropping. The system model considers multiple multi-antenna eavesdroppers, whose channel state information (CSI) is imperfectly known.
2. Optimization Problem: A non-convex optimization problem is formulated to jointly design beamformers, artificial noise (AN) covariance matrices, and IRS phase shifters. The goal is to maximize system sum-rate while limiting information leakage. This involves addressing the challenge of non-convexity due to unit modulus constraints from IRS phase shifts and CSI uncertainties.
3. Algorithmic Solution: The paper introduces an efficient algorithm based on alternating optimization, semidefinite relaxation (SDR), and successive convex approximation (SCA). The proposed approach ensures a stationary solution, effectively tackling the non-convex constraints by transforming the problem into a series of tractable forms.
4. Numerical Results and Insights: Simulation results confirm the significant improvement in secrecy performance when IRSs are deployed. The findings suggest that a uniform distribution of reflecting elements across multiple IRSs enhances physical layer security more effectively than concentrating elements in a single IRS.

Practical and Theoretical Implications

• Enhancement of Physical Layer Security:

The deployment of IRSs offers a cost-effective solution for significantly improving physical layer security by adapting to challenging propagation environments. This is particularly beneficial in scenarios where conventional direct communication is compromised.

• Robustness Against CSI Uncertainty:

The robust design approach accounts for CSI uncertainties, ensuring that the system remains secure even when the exact eavesdroppers’ CSI is unavailable.

Future Research Directions

• Integration with Emerging Technologies:

Future studies could explore the integration of IRS-assisted systems with other advanced communication frameworks, such as massive MIMO and 6G networks, to further bolster security and performance.

• Dynamic and Real-time IRS Configuration:

Investigating real-time IRS configuration and adaptive algorithms could enhance system flexibility and responsiveness to varying environmental conditions and threats.

• Machine Learning for Secure Optimization:

Leveraging machine learning techniques for optimizing IRS configurations in dynamic environments could offer improvements in both computational efficiency and security outcomes.

The paper presents a comprehensive approach to secure wireless communications using IRSs, providing a significant contribution to the field by addressing a practical security challenge with advanced signal processing and optimization techniques.