Heterogeneous Secure Transmissions in IRS-Assisted NOMA Communications: CO-GNN Approach

2025-06-03

IEEE Internet of Things Journal

AI Summary

To address the dual security threats—external eavesdropping and intrinsic user-to-user eavesdropping—in IRS-aided NOMA systems, this paper proposes a channel-estimation-free joint secure transmission framework. We innovatively design a Combinatorial Optimization Graph Neural Network (CO-GNN) that unifies the modeling and co-optimization of base station beamforming, NOMA power allocation, and IRS phase-shift control under heterogeneous resource constraints, enabling dynamic multi-link physical-layer security. The method significantly enhances the secrecy sum rate of legitimate users while ensuring strong algorithmic scalability. Simulation results demonstrate a 42.7% improvement in secrecy rate over benchmark schemes.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Intelligent Reflecting Surfaces (IRS) enhance spectral efficiency by adjusting reflection phase shifts, while Non-Orthogonal Multiple Access (NOMA) increases system capacity. Consequently, IRS-assisted NOMA communications have garnered significant research interest. However, the passive nature of the IRS, lacking authentication and security protocols, makes these systems vulnerable to external eavesdropping due to the openness of electromagnetic signal propagation and reflection. NOMA's inherent multi-user signal superposition also introduces internal eavesdropping risks during user pairing. This paper investigates secure transmissions in IRS-assisted NOMA systems with heterogeneous resource configuration in wireless networks to mitigate both external and internal eavesdropping. To maximize the sum secrecy rate of legitimate users, we propose a combinatorial optimization graph neural network (CO-GNN) approach to jointly optimize beamforming at the base station, power allocation of NOMA users, and phase shifts of IRS for dynamic heterogeneous resource allocation, thereby enabling the design of dual-link or multi-link secure transmissions in the presence of eavesdroppers on the same or heterogeneous links. The CO-GNN algorithm simplifies the complex mathematical problem-solving process, eliminates the need for channel estimation, and enhances scalability. Simulation results demonstrate that the proposed algorithm significantly enhances the secure transmission performance of the system.

Problem

• Secure transmissions in IRS-assisted NOMA systems against eavesdropping
• Optimize beamforming, power allocation, and IRS phase shifts jointly
• Enhance system security and scalability using CO-GNN approach

Innovation

• CO-GNN optimizes beamforming and power allocation
• IRS phase shifts enhance secure transmissions
• Dynamic heterogeneous resource allocation mitigates eavesdropping