Adaptive user clustering for downlink nonorthogonal multiple access based 5G systems using Brute-force search

Nonorthogonal multiple access (NOMA) has been envisaged as a potential candidate for the forthcoming 5G cellular networks and beyond 5G networks. The existing user clustering schemes in NOMA systems exploit the channel heterogeneity and channel diversity to partition the users into different clusters by grouping the same number of users to each cluster. Due to the constraint of having the fixed number of users in each cluster, the channel heterogeneity and diversity cannot be fully explored, which causes the existing user clustering scheme to perform poorly in terms of throughput performance. In this article, an efficient and dynamic clustering method termed adaptive user clustering (AUC), which flexibly group the users to different clusters based on their channel conditions regardless of the cluster size, is proposed. The channel heterogeneity and diversity are fully exploited in user grouping that maximizes the system throughput. The clustering mechanism of the proposed AUC scheme is performed using the Brute-force search (B-FS) method by searching through all the possible partitions for the best partition with the highest throughput. Simulation results obtained demonstrate that the proposed AUC scheme using the B-FS method always outperforms the existing user grouping approaches in various network scenarios in terms of throughput performance.