Secure THz Communication in 6G: A Two-Stage DRL Approach for IRS-Assisted NOMA

The rapid evolution of 6G networks demands innovative solutions to address the dual challenges of ensuring robust physical layer security (PLS) and optimizing energy efficiency. This paper introduces an innovative framework based on deep reinforcement learning (DRL) for enhancing security and energy efficiency in terahertz (THz) communication within intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) systems. Utilizing the deep deterministic policy gradient (DDPG) algorithm, we introduce a two-stage policy learning approach designed to optimize secrecy energy efficiency (SEE) while ensuring secure communication, even in the presence of multiple eavesdroppers (Eves). The first stage focuses on optimizing the secrecy rate (SR) by dynamically adjusting IRS phase shifts and transmit power allocation, ensuring secure communication links. The second stage refines the policy to minimize energy consumption without compromising the achieved security. Our framework addresses the challenges of imperfect channel state information (CSI) and inherent uncertainties in THz communication, making it highly adaptable to real-world scenarios. Through extensive simulations, we demonstrate that the proposed DDPG-based approach outperforms traditional methods such as advantage actor-critic (A2C) and alternating optimization (AO) in terms of convergence speed, stability, and final SEE performance. The results highlight the effectiveness of IRS in enhancing signal reflection and coverage, significantly improving both security and energy efficiency in THz-based 6G networks. This work provides a scalable and sustainable solution for next-generation wireless communication systems, paving the way for secure, energy-efficient, and high-capacity 6G networks.