论文标题
mmwave-noma系统的分层用户聚类
Hierarchical User Clustering for mmWave-NOMA Systems
论文作者
论文摘要
非正交的多重访问（NOMA）和MMWAVE是两种补充技术，可以支持5G和网络之外产生的容量需求。同时为用户提供越来越多的用户，同时为带宽的稀缺提供了解决方案。在本文中，我们提出了一种将用户聚集在MMWave-Noma系统中的方法，目的是最大化总和率。使用了一种无监督的机器学习技术，即使用层次聚类，可以自动识别最佳簇数。该模拟证明，与其他聚类方法（例如K-Means clustering）相比，所提出的方法可以最大化系统的总和，同时满足所有用户的最小QoS，而无需将簇数作为先决条件。
Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond networks. The increasing number of users are served simultaneously while providing a solution for the scarcity of the bandwidth. In this paper we present a method for clustering the users in a mmWave-NOMA system with the objective of maximizing the sum-rate. An unsupervised machine learning technique, namely, hierarchical clustering is utilized which does the automatic identification of the optimal number of clusters. The simulations prove that the proposed method can maximize the sum-rate of the system while satisfying the minimum QoS for all users without the need of the number of clusters as a prerequisite when compared to other clustering methods such as k-means clustering.
