IAFIR Optimization against NOMA Competitors: A Comprehensive Analysis on Energy-Efficient Non-Orthogonal Multiple Access

The ever-growing demands of next-generation wireless networks necessitate the exploration of innovative technologies to enhance spectral efficiency, user experience and network performance. Non-Orthogonal Multiple Access (NOMA) has emerged as a promising candidate, offering the potential for increased capacity and improved system reliability. NOMA allows multiple users to share the same time-frequency resource, breaking away from traditional orthogonal access methods. In NOMA, users are distinguished by different power levels, enabling simultaneous transmission and reception. This novel approach presents advantages in scenarios with a high density of connected devices, such as urban environments and Internet of Things (IoT) deployments, contributing to enhanced spectral efficiency. However, NOMA is not without limitations. One key challenge lies in the complexity of managing interference among users sharing the same resource, particularly as the number of users increases. Ensuring fairness and maintaining a balance in user quality of service becomes a critical concern. Efficient algorithms and schemes, such as the Interference Adaptive Fusion with Iterative Rejection (IAFIR), aim to address these limitations and enhance NOMA performance. The study focuses on the IAFIR scheme, a novel approach designed to mitigate interference and enhance the quality of service in NOMA scenarios. To comprehensively assess the effectiveness of IAFIR and its impact on next-generation wireless networks, a comparative analysis against other well-established NOMA techniques, including Iterative Interference Cancellation (IIC) and Interference Rejection Combining (IRC), is conducted. By understanding their performance characteristics and their applicability to various wireless network scenarios, the goal is to guide the development and deployment of more efficient and reliable next-generation wireless networks. The research lays the groundwork for potential extensions, particularly in the context of multiuser scenarios, where the performance of IAFIR can be evaluated in more complex environments.