A Framework of Mahalanobis-Distance Metric with Supervised Learning for Clustering Multipath Components in MIMO Channel Analysis

@article{Chen2022AFO,
  title={A Framework of Mahalanobis-Distance Metric with Supervised Learning for Clustering Multipath Components in MIMO Channel Analysis},
  author={Yi Chen and Chong Han and Jia He and Guangjian Wang},
  journal={ArXiv},
  year={2022},
  volume={abs/2110.08768}
}
As multipath components (MPCs) are experimentally observed to appear in clusters, cluster-based channel models have been focused in the wireless channel study. However, most of the MPC clustering algorithms for MIMO channels with delay and angle information of MPCs are based on the distance metric that quantifies the similarity of two MPCs and determines the preferred cluster shape, greatly impacting MPC clustering quality. In this paper, a general framework of Mahalanobisdistance metric is… 
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