User Clustering Techniques for Massive MIMO-NOMA Enabled mmWave/THz Communications in 6G

Abstract

Recently, Cooperative massive multiple-input multiple-output and non-orthogonal multiple access (mMIMO-NOMA) has been considered as a promising solution that can significantly improve the system capacity and the spectral efficiency of the sixth-generation (6G) high frequency spectrum such as Millimeter Wave and Terahertz networks. In this paper, we consider a mMIMO-NOMA enabled base station that can support a number of single antenna users in different clusters. Cooperative use of NOMA can support the users in a cluster by sharing the same frequency and time resources. However, in 6G the networks will be congested with ultra-massive interconnected users and that arises challenges in clustering the users efficiently. Therefore. we briefly summarize the studies about user clustering solutions in mMIMO-NOMA systems and divided them into two categories; resource aware user clustering (RAUC) and learning assisted user clustering (LAUC) approaches. A comparison among those techniques has been tabulated considering the computational complexities. The result depicts that the RAUC demonstrates a polynomial complexity function while that for the LAUC is comparatively low.