Abstract
This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.
Highlights
Clustering is a process that analyses data by classifying groups with similar structures
Datasets for feature selection, intrusion detection, white blood cell classification and wireless sensor networks have been clustered over the years [1,2,3,4]
The indoor channel scenarios had better accuracy for both the membership of clusters and the cluster count compared with the semi-urban channel scenarios
Summary
Clustering is a process that analyses data by classifying groups with similar structures. Clustering aims to categorize the data into several clusters such that points in the same group are similar while that of the other groups are dissimilar. Datasets for feature selection, intrusion detection, white blood cell classification and wireless sensor networks have been clustered over the years [1,2,3,4]. Clustering of wireless propagation multipaths gained interest due to the widespread application of multiple-input multiple-output (MIMO) antennas in wireless communications systems [5,6]. MIMO systems are developed to increase data rates and ensure wireless transmission reliability [7]. Clusterbased channel models are used to develop the MIMO propagation channel [8]
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