Browse
Recent Submissions
Item AI based NOMA for Heterogeneous Networks(UMT, Lahore, 2025) Syed Muhammad HamedoonAs wireless networks evolve to accommodate increasing user density, effective user clustering techniques are essential for optimizing resource allocation in Non-Orthogonal Multiple Access (NOMA) systems. Thus, as high-speed and quick data access becomes increasingly available, the need for a sophisticated and enhanced wireless network begins to emerge. NOMA-based heterogeneous networks are essential for the Internet of Things (IoT) era as they combine multiple wireless technologies and devices to enhance coverage, capacity, and user quality of service. In heterogeneous IoT networks with many users, exhaustively searching for optimal user pairs or groups becomes computationally infeasible. As user density increases, clustering becomes a combinatorial problem that demands scalable, low-complexity solutions. This research indicates various user partitioning algorithms that enhance network performance, spectral efficiency, and user fairness, particularly in dense scenarios with diverse channel conditions. We address the challenges of user clustering and power allocation in multi-carrier NOMA systems, emphasizing the importance of energy efficiency for IoT devices. A novel user clustering approach based on partial brute force search (P-BFS) is proposed, significantly reducing complexity while improving throughput. Additionally, we explore a Reconfigurable Intelligent Surface (RIS)-assisted NOMA framework that optimizes power allocation and phase shifts through advanced optimization techniques, including deep learning and reinforcement learning. RIS-assisted NOMA systems designed for IoT networks make better use of spectrum, which saves power and keeps connections between many users stable. This system differs from conventional wireless communication systems. This research uses AI-based methods to find the best balance between sum rate and energy efficiency, finding the best RIS phase shifts and power distribution. The results demonstrate substantial improvements in sum rate and energy efficiency, highlighting the potential of intelligent clustering methods for future 5G and beyond networks.