A Selective Secure Precoding Framework for MU-MIMO Rate-Splitting Multiple Access Networks Under Limited CSIT

2024-12-26

AI Summary

This work addresses a complex physical-layer security scenario in multi-user MIMO downlink systems, where legitimate users require both private and common messages, multiple eavesdroppers coexist, and only limited channel state information at the transmitter (CSIT) is available. To jointly accommodate heterogeneous security requirements, we propose a selective secure precoding framework—the first to unify modeling of such mixed confidentiality constraints. To tackle the resulting non-convex, non-smooth sum secrecy spectral efficiency maximization problem, we introduce a LogSumExp smooth approximation and leverage generalized eigenvalue conditions, integrating conditional average rate modeling with a power iteration method for robust optimization under finite CSIT. Experiments demonstrate stable convergence, significant gains in secrecy spectral efficiency under multi-eavesdropper settings, and consistent performance improvements over RSMA-based baselines—validating both theoretical soundness and practical applicability.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Privacy
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In this paper, we propose a robust and adaptable secure precoding framework designed to encapsulate a intricate scenario where legitimate users have different information security: secure private or normal public information. Leveraging rate-splitting multiple access (RSMA), we formulate the sum secrecy spectral efficiency (SE) maximization problem in downlink multi-user multiple-input multiple-output (MIMO) systems with multi-eavesdropper. To resolve the challenges including the heterogeneity of security, non-convexity, and non-smoothness of the problem, we initially approximate the problem using a LogSumExp technique. Subsequently, we derive the first-order optimality condition in the form of a generalized eigenvalue problem. We utilize a power iteration-based method to solve the condition, thereby achieving a superior local optimal solution. The proposed algorithm is further extended to a more realistic scenario involving limited channel state information at the transmitter (CSIT). To effectively utilize the limited channel information, we employ a conditional average rate approach. Handling the conditional average by deriving useful bounds, we establish a lower bound for the objective function under the conditional average. Then we apply the similar optimization method as for the perfect CSIT case. In simulations, we validate the proposed algorithm in terms of the sum secrecy SE.

Problem

• Secure Coding
• Multi-user Network
• Cryptography

Innovation

• Rate-Splitting Multiple Access (RSMA)
• LogSumExp Technique
• Conditional Average Rate Strategy