Developing wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface (IRS)-enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). The IRS facilitates secure transmission for the primary transmitter (PT) by intelligently adjusting the phase shifts of the signals from the PT, while simultaneously transmitting its own data to an Internet of Things (IoT) device. In light of the existence of numerous eves and unpredictable channels in real-world situations, we concurrently optimize the active beamforming of the PT and the phase shifts of the IRS to enhance the secrecy of IRS-assisted secure relay networks while adhering to quality-of-service standards and secure communication rates. To address this intricate non-convex stochastic optimization issue, we propose a secure beamforming technique named DDPG-SR, utilizing an effective deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) scheme to determine the optimal beamforming approach against Eves. This method seeks to establish an optimal beamforming strategy to counteract Eves under dynamic environmental circumstances. Comprehensive simulation experiments confirm the effectiveness of our proposed solution, showcasing enhanced performance relative to conventional IRS methods, IRS backscattering-based anti-evesdropping techniques, and other benchmark tactics for secrecy performance.

The communication network of Unmanned Aerial Drones (UAD) is expected to become a vital element in the development of next-generation wireless networks, offering flexible infrastructure that extends network coverage to remote or disaster-stricken locations while enhancing capacity during critical events and large-scale emergencies. As UAD technology evolves, its role in ensuring consistent, widespread connectivity becomes more essential, though it faces challenges such as high latency, low spectral efficiency, and fairness issues across multiple drones. This research presents an optimization framework designed for multi-UAD communication networks based on Non-Orthogonal Multiple Access (NOMA) to address these difficulties. The framework focuses on optimizing ground user-to-UAD associations and drone power allocation to maximize spectral efficiency. The primary optimization problem is a mixed-integer, nonconvex, and nonlinear task, which seeks to maximize the sum-rate while addressing issues of UAD-user association and power distribution, complicated by interference and binary decision variables. To manage this complexity, we first optimize UAD-user associations under fixed NOMA power allocation and then optimize the power allocation for each NOMA-enabled ground user connected to the drones. Our numerical results show that this framework provides better performance than traditional orthogonal multiple access (OMA)-based optimization methods and other benchmark NOMA-based techniques, offering improved spectral efficiency, lower complexity, and faster convergence, making it an effective solution for enhancing UAD network performance across a range of dynamic scenarios.