This paper focuses on downlink power allocation for a cognitive radio-based non-orthogonal multiple access (CR-NOMA) system in a femtocell environment involving device-to-device (D2D) communication. The proposed power allocation scheme employs the greedy asynchronous distributed interference avoidance (GADIA) algorithm. This research aims to optimize the power allocation in the downlink transmission, considering the unique characteristics of the CR-NOMA-based femtocell D2D system. The GADIA algorithm is utilized to mitigate interference and effectively optimize power allocation across the network. This research uses a fairness index to present a novel fairness-constrained power allocation algorithm for a downlink non-orthogonal multiple access (NOMA) system. Through extensive simulations, the maximum rate under fairness (MRF) algorithm is shown to optimize system performance while maintaining fairness among users effectively. The fairness index is demonstrated to be adaptable to various user counts, offering a specified range with excellent responsiveness. The implementation of the GADIA algorithm exhibits promising results for sub-optimal frequency band distribution within the network. Mathematical models evaluated in MATLAB further confirm the superiority of CR-NOMA over optimum power allocation NOMA (OPA) and fixed power allocation NOMA (FPA) techniques.

Future 6^th Generation (6G) networks will rely on Terahertz (THz) wireless communication as their main enabler for delivering both ultra-high data speed and minimal delay. THz wireless systems become crucial for upcoming communications by using Unmanned Aerial Vehicles (UAVs) together with Intelligent Reflecting Surfaces (IRS) while improving reliability and efficiency. In UAV-IRS-assisted networks, minimizing mission completion time and energy consumption is critical. However, achieving rapid mission execution often requires UAVs to operate at higher speeds, increasing energy usage and creating a trade-off that demands optimization. This paper addresses the challenge of optimizing UAV-IRS trajectories in THz networks to reduce mission time while adhering to energy constraints. Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. MADRL dynamically adapts UAV-IRS paths, balancing mission efficiency and energy savings. Simulation results demonstrate that the proposed MADRL-based approach outperforms existing benchmarks, achieving shorter mission times and near-optimal energy consumption across varying scenarios. By leveraging cooperative learning, the algorithm effectively handles complex environments with multiple users and IRS elements. This work highlights the potential of MADRL for UAV-IRS trajectory optimization, offering a scalable solution for energy-efficient and high-performance THz communication systems.

Terahertz (THz) communication networks, combined with Intelligent Reflecting Surfaces (IRS), hold significant potential for advancing the capabilities of Sixth-Generation (6G) wireless systems. The integration of these technologies offers opportunities for ultra-fast data transmission and substantial improvements in network capacity, meeting the growing demand for higher bandwidth and faster connectivity. This study investigates the use of flying IRS-assisted Unmanned Aerial Vehicles (UAVs) within a THz communication framework, aiming to overcome challenges in environments with complex signal propagation, such as urban settings. To facilitate this integration, we introduce the Fly-IRS algorithm, designed to optimize key aspects of network performance, including user device grouping, IRS phase shifts, and UAV positioning. These factors are critical for maximizing data transmission rates, boosting network capacity, and minimizing service disruptions that affect user experience. Our approach leverages a combination of Multi-Agent Deep Reinforcement Learning (MADRL) and Particle Swarm Optimization (PSO) to effectively handle user grouping, IRS phase shift optimization, and UAV trajectory planning. By merging the strengths of both techniques, the Fly-IRS algorithm enhances system adaptability and efficiency. Simulation results demonstrate significant improvements in data rates, with the Fly-IRS algorithm delivering up to 10% better performance compared to the MADRL method alone. These findings highlight the transformative potential of integrating THz communication networks with IRS and UAV technologies, emphasizing their ability to shape future wireless communication systems. This innovation could lead to more reliable, high-capacity networks, capable of addressing the advanced requirements of 6G, offering improved service quality even in difficult environments.