Towards intelligent user clustering techniques for non-orthogonal multiple access: a survey

Abstract

With the increasing user density of wireless networks, various user partitioning techniques or algorithms segregate users into smaller, more manageable clusters. The benefit of user clustering techniques in non-orthogonal multiple access (NOMA) is to optimize resource allocation and improve network performance, spectral efficiency, and user fairness in next-generation wireless networks, particularly in scenarios with a high density of users and diverse channel conditions. With increasing users, the network creates clusters before implementing non-orthogonal multiple access within these clusters. In this paper, we have organized and classified various user clustering techniques deployed from the perspective of NOMA-based communication in the current era. Furthermore, researchers have highlighted some works deploying joint resource allocation and clustering optimization based on various criteria to enhance the overall sum rate of the network. We also identify low-complexity user clustering techniques for multiple applications, e.g. the Internet of Things, unmanned aerial vehicles, and reconfigurable intelligent surfaces in the 5G and beyond communication networks.