User Clustering Scheme for Downlink Hybrid NOMA Systems Based on Genetic Algorithm

Abstract

Non-orthogonal multiple access (NOMA) is considered to be a promising technology for improving bandwidth utilization efficiency and reducing power consumption. In this paper, we consider the optimization of user clustering in the NOMA scenario, where the goal is to maximize the system total throughput under minimum rate constraints. Different from most existing literatures, there is no limit on the number of users in each cluster. Simulation results show that the proposed scheme can significantly reduce computational complexity and have a better performance compared with schemes based on the other heuristic algorithms and random user clustering with greedy strategy.