Cross-Layer Secure Sum-Rate Maximization in IRS–NOMA O-RAN

The integration of intelligent reflecting surfaces (IRS) and non-orthogonal multiple access (NOMA) within the Open Radio Access Network (O-RAN) offers significant opportunities for 6G Internet of Things (IoT) systems but also raises new challenges in security, reliability, and resource efficiency. In this paper, we propose a cross-layer secure sum-rate maximization framework that jointly addresses physical-layer secrecy and network-layer packet loss in IRS–NOMA O-RAN environments. We analytically derive closed-form expressions for secrecy rates and packet loss under a cascaded Rician fading model, and formulate a secrecy sum-rate optimization problem that accounts for IRS phase shifts, NOMA power allocation, and energy- harvesting constraints. The resulting problem is NP-hard due to non-convex coupling across layers. To overcome this, we develop SecureO-RAN-SAC, a deep reinforcement learning algorithm based on Soft Actor-Critic v2, which learns near-optimal policies in real time. Simulation results demonstrate that SecureO-RAN- SAC achieves comparable or superior performance to grid-based search (GBS) while requiring only ∼10% of its computational cost for a 64-element IRS. These findings highlight the scalability and efficiency of our approach, establishing a new paradigm for secure, resource-aware, and ML-driven cross-layer optimization in O-RAN-enabled IoT networks.