Hierarchical User Clustering for mmWave-NOMA Systems

Abstract

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.

User Clustering in mmWave-NOMA Systems With User Decoding Capability Constraints for B5G Networks

“…The BS can use the cosine similarity metric between the user's channel vector and the precoding vector of each beam to determine the level of correlation between the user and the beam. This metric has been used in several mmWave-NOMA works for user clustering to determine the correlation between users in [5], [20], and between users and random beams in [17]. Using similar steps as these works, we can derive the cosine similarity between a user-u with channel h u and a beam-b with precoding vector w b here as follows:…”

“…In [19], the authors use an angledomain NOMA scheme that schedules one cell-center and one cell-edge user in a NOMA pair, for each beam in each cell. Recent works in mmWave-NOMA systems have also used machine learning clustering techniques to identify correlated users and group them in NOMA clusters [5], [20], [21]. Further, in mmWave systems, since it is often infeasible to scale up the number of transceivers with the number of antennas, studies in mmWave-NOMA systems often use either analog beamforming (BF) with a single RF chain [16], [22], [23] or a hybrid BF design with a reduced number of RF chains [24], [25].…”

Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems

“…Unsupervised machine-learning-based algorithms were used for user clustering in mmWave-NOMA in [10,35,36]. In these three studies, the user locations were distributed in accordance with the PCP, whereby users are physically clustered as in restaurants, coffee shops, and hotels.…”

“…Furthermore, the authors derived an optimal PA to maximize the NOMA sum-rate. In [36], an approach for user clustering in the mmWave-NOMA system was presented for sum-rate maximization. In this approach, hierarchical clustering is applied to satisfy the initial requirement without knowing the number of clusters, which is appropriate for user distribution in an area.…”

“…In mmWave-NOMA systems, in order to reduce the inter-beam interference, it is desired that the users in the same beam remain close to each other or have strongly correlated channel gains, and that users in different beams remain distant from each other or have weakly correlated channel gains. Most studies focus on grouping users with strongly correlated channel gains [10,36]. In this study, we exploit the spatial distribution of users around the BS and group users who remain close together.…”