SVM-Assisted Adaptive Kernel Power Density Clustering Algorithm for Millimeter Wave Channels

Abstract

Cluster-based channel modeling has gradually become a trend in the development of a channel model, since it is a good compromise between accuracy and complexity. However, most of the existing clustering algorithms require prior knowledge of clusters, initialization, and threshold choices. An accurate and automatic cluster identification algorithm is therefore highly desirable for channel modeling. In this article, adaptive kernel-power-density (AKPD) and support vector machine-assisted AKPD (SVM-AKPD) algorithms are proposed. First, a new distance-based metric is proposed to calculate an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive-K</i> for each multipath component (MPC), in which the AKPD can be used in scenarios where we have a complex distribution of MPCs, especially for the cluster with small MPCs. Furthermore, the SVM is applied in clustering by the full partition of MPCs’ feature space to overcome the limitation of the AKPD, where the MPCs lying at a large distance from the cluster centroids will be clustered into surrounding clusters when the clusters are closely spaced in the AKPD. Finally, the performance of the proposed AKPD and SVM-AKPD is validated with measured and simulated channels data at millimeter waveband, respectively. Both numerical simulations and experimental validation results are provided to demonstrate the effectiveness and robustness of the proposed algorithm. The proposed algorithms enable applications in multiple-input–multiple-output (MIMO) channels with no prior knowledge about the clusters, such as number and initial locations. It also does not need to adjust cluster parameters manually and can be implemented for cluster-based channel modeling with a fairly low complexity.