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A Kernel-Power-Density-Based Algorithm for Channel Multipath Components Clustering
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DOI：10.1109/TWC.2017.2740206.png)
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Abstract
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Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multiple-input-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for cluster-based channel modeling.
Keywords:
Channel measurement and modeling
clustering analysis
kernel density
machine learning
multipath component
wireless channel
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