GWO-Based User Clustering And Power Allocation For Downlink MIMO-NOMA Systems

Due to the rapid technological progress and the growing demand for massive connectivity, cutting-edge technologies, including massive Multi-Input Multi-Output (mMIMO) and Non-Orthogonal Multiple Access (NOMA), are essential to optimize beyond 5G networks. Efficient resource allocation strategies, such as user clustering and power allocation techniques, considerably improve the performance of downlink MIMO-NOMA wireless communication systems. Despite their notable advantages, these methods lead to computational complexity challenges driven by multiple variables and constraints. Therefore, we introduce a framework based on Grey Wolf Optimizer (GWO) to perform power allocation and user clustering. The adaptability and efficiency of this meta-heuristic optimization approach enable our method to handle the massive access scenario by maximizing the system capacity and improving energy efficiency (EE) and spectral efficiency (SE). Simulation results demonstrate the superiority of this GWO-based approach, exhibiting significant improvements in the number of served users and a decrease in the transmit power compared to conventional resource allocation techniques. This study marks a significant step forward in optimizing the MIMO-NOMA system. It sets the stage for further advancements in resource allocation strategies and potentially revolutionizes 5 G and beyond wireless communications.