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

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.

The spatiotemporal movement of patients in and out of a psychiatric hospital: an observational GPS study

“…GPS data for one patient on one particular day were only included if ≥ 1000 signals were available, which corresponded to GPS data for at least 1.4 h per day. The theoretically maximum number of GPS points was 17,280 for 24 h with recordings every 5 s. Subsequently, the ST-SBCAN algorithm – a state-of-the-art density-based clustering algorithm [ 14 ] – was applied individually for each patient and day and the obtained spatiotemporal clusters were merged with the GPS coordinates of the hospital (in the case of inpatient) and home (in case of outpatient) location of each patient. Coordinates of the hospital and home were defined in decimal degrees, and all destinations with centroid coordinates within a radius of 200 m of the hospital or home coordinates were given the respective label.…”

Spatial–temporal grid clustering method based on frequent stay point recognition

“…The DPCG have studied the problem of efficiently mining the location of users' interest points and frequent travel sequences in a given spatial region from a large amount of GPS trajectory data [12,13]. Through the trip test design, collected the individual complete trip link GPS data, three kinds of algorithms, including rule-based algorithm [14,15], density-based spatial clustering algorithm [16,17] and density-based spatiotemporal clustering algorithm [18,19], are used to identify and evaluate travel end points that recognize most types of travel [20,21]. The above algorithms summarize clustering algorithms from different aspects to improve the identification of stopping point, but the density of different classes is different, the neighborhood is difficult to set, and the efficiency of processing a large number of data is still lacking.…”