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

TL;DR

This paper proposes a CO-GNN-based method to optimize secure transmissions in IRS-assisted NOMA systems, effectively mitigating eavesdropping risks and improving secrecy rates through joint beamforming, power allocation, and IRS phase shift optimization.

Contribution

It introduces a novel CO-GNN approach for joint optimization in IRS-assisted NOMA, addressing security challenges without requiring channel estimation.

Findings

Significant increase in secrecy rates with the proposed method.

Effective mitigation of external and internal eavesdropping.

Enhanced scalability and reduced complexity in secure transmission design.

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.