Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface (2002.07063v4)

Abstract: This paper considers a MIMO secure wireless communication system aided by the physical layer security technique of sending artificial noise (AN). To further enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked in the AN-aided communication system, where the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the TPC matrix, AN covariance matrix, and phase shifts while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Finally, simulation results validate the effectiveness of system security enhancement via an IRS.

• The paper introduces IRS technology to bolster secure MIMO communications by integrating artificial noise, significantly enhancing secrecy rates.
• It employs a block coordinate descent algorithm with MM-based phase shift optimization to jointly design transmit precoding and AN strategies.
• Numerical results show that increasing IRS elements and strategic placement improve secrecy, particularly in multicast scenarios with multiple receivers.

Overview of Artificial-Noise-Aided Secure MIMO Wireless Communications with IRS

This paper presents a paper on leveraging Intelligent Reflecting Surfaces (IRS) to enhance the security of Multiple-Input Multiple-Output (MIMO) wireless communication systems by artificial noise (AN) techniques. The authors focus on optimizing the secrecy rate (SR) by jointly configuring the transmit precoding (TPC) matrix at the base station (BS), AN covariance matrix, and IRS phase shifts. This work addresses a non-convex problem with interdependent variables, providing solutions through a block coordinate descent (BCD) algorithm. The paper expands the SRM problem to scenarios involving multiple legitimate information receivers (IRs).

Key Contributions:

1. IRS Integration in MIMO Systems: This research introduces IRS technology to bolster physical layer security for AN-aided MIMO systems, a novel application not extensively covered in existing literature. The IRS is used to reconfigure the wireless propagation environment, enhancing channel quality between BS and IR while degrading transmission to potential eavesdroppers (Eve).
2. Optimization Approach: The secrecy rate maximization (SRM) is constructed as a block coordinate descent algorithm, reformulating the original optimization problem using MMSE principles. This transforms the problem into a more tractable form, allowing efficient iterative updates for TPC, AN, and IRS phase shifts. The approach leverages the Lagrangian multiplier method to derive near-optimal solutions, and the Majorization-Minimization (MM) algorithm is used for iterative phase shift optimization.
3. Extension to Multiple IRs: The paper extends its analysis to a multicast scenario involving several IRs, adapting the BCD algorithm to cater to this complexity. This part of the research proposes solving the new multicast SRM problem using a BCD method robust enough to manage varying channel difficulties and interactions across multiple receivers.

Numerical Results & Algorithm Efficiency:

Simulations affirm that IRS can substantially enhance MIMO transmission security. The paper reveals that increasing IRS elements leads to higher secrecy rates, as more constructively aligned reflected signals reach the IR. Additionally, results show deploying IRS closer to the IR significantly improves system security, confirming the strategic importance of IRS placement.

The convergence is rapid, with the proposed BCD-MM scheme outperforming benchmark approaches, exhibiting scalability and stronger SR across increased IRS configurations and power constraints. The analysis indicates the benefits of optimizing IRS deployment, considering environmental factors like path loss and practical limitations such as discrete phase shifts.

Theoretical & Practical Implications:

This paper contributes to both theoretical and practical domains in secure wireless communications. The analytical techniques deployed and the results obtained reinforce the significance of phase shift optimization and IRS strategic deployment. Future work could investigate advanced IRS designs and explore deeper integration with emerging technologies like artificial intelligence for adaptive configuration and learning-based optimization.

Future Directions:

With the growing adoption of 6G technologies, the role of IRS in secure MIMO systems will become increasingly pivotal. Further research could delve into dynamic and real-time IRS adjustments within busy networks, exploring how machine learning models can predict and react to varying security threats more efficiently than traditional methods.

Future investigations might also explore multi-cellular IRS applications or the interactions between multiple IRS panels to optimize joint network efficacy and security. This broader perspective could encompass cross-layer security, incorporating novel cryptographic techniques alongside physical layer advancements, potentially revolutionizing secure communication frameworks.