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

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 multi-in–multiout (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 article, a general framework of Mahalanobis-distance metric is proposed for MPC clustering in the MIMO channel analysis, without user-specified parameters. Remarkably, the popular multipath component distance (MCD) is proven to be a special case of the proposed distance metric framework. Furthermore, two machine learning algorithms, namely, weak-supervised Mahalanobis metric for clustering and supervised large margin nearest neighbor, are introduced to learn the distance metric. To evaluate the effectiveness, a modified channel model is proposed based on the Third Generation Partnership Project (3GPP) spatial channel model (SCM) to generate clustered MPCs with delay and angular information since the original 3GPP SCM is incapable to evaluate clustering quality. Experiment results show that the proposed distance metric can significantly improve the clustering quality of existing clustering algorithms, while the learning phase requires considerably limited efforts of labeling MPCs.