  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 Mahalanobis-distance metric is proposed for MPC clustering in MIMO
channel analysis, without user-specified parameters. Remarkably, the popular
multipath component distance (MCD) is proved 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 3GPP spatial channel model to generate clustered MPCs with delay
and angular information, since the original 3GPP spatial channel model (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.
