Abstract
The combination of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) systems exploits the capability to serve multiple user devices simultaneously in one resource block. User clustering, power allocation (PA), and hybrid beamforming problems in mmWave-NOMA systems can utilize the network setting’s potential to enhance the system performance. Based on similar characteristics of the spatial distributions of users in real life, we propose a novel spatial-temporal density-based spatial clustering of applications with noise (ST-DBSCAN)-based unsupervised user clustering in order to enhance the system sum-rate. ST-DBSCAN is a state-of-the-art density-based clustering algorithm for solving spatial and non-spatial problems. Moreover, instead of symmetric PA, we propose an inter-cluster PA algorithm. Next, we apply boundary-compressed particle swarm optimization in order to reduce inter-cluster interference and enhance system performance. The simulation results reveal that our proposed solution improves the sum-rate of mmWave-NOMA-based systems when compared with that of mmWave-OMA-based systems. In addition, we compare our proposed algorithm with other benchmark user clustering algorithms in order to investigate the performance of our ST-DBSCAN-based user clustering algorithm. The results also illustrate that our proposed approach outperforms the state-of-the-art user clustering algorithms in mmWave-NOMA systems.
Highlights
In the last decade, with the substantial traffic development and mobile communication growth, the number of mobile devices in use has increased rapidly, which necessitates a corresponding increment in the required bandwidth [1]
The authors investigated four distinct user pairing algorithms in heterogeneous wireless networks in [25]. The findings of these studies indicate that the performance of non-orthogonal multiple access (NOMA) systems can be significantly improved through user pairing
We considered a downlink mmWave-NOMA system with an hybrid beamforming (HBF) structure
Summary
With the substantial traffic development and mobile communication growth, the number of mobile devices in use has increased rapidly, which necessitates a corresponding increment in the required bandwidth [1]. Inspired by the characteristics of machine learning and novel techniques of clustering, we develop an unsupervised learning-based user clustering method for mmWave-NOMA systems. To solve the user clustering problem, we propose a novel spatial–temporal density-based spatial clustering of applications with noise (ST-DBSCAN)-based algorithm for user clustering in downlink mmWave-NOMA systems. When the users are in close proximity to each other, the BS can form a narrower beam in order to achieve a higher beam gain than that achievable with a wider beam [9] By exploiting this characteristic, our ST-DBSCAN-based user clustering algorithm can enhance the performance of mmWave-NOMA systems.
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