A Multidimensional Feature Metric-Based Cluster-Tracking Algorithm and Its Application to Time-Varying Millimeter-Wave Channels

In wireless communication, time-varying channel modeling has been an essential research topic. In this communication, a multidimensional feature metric-based (MFM) cluster-tracking algorithm is proposed. First, the multidimensional features (including centroid, shape, and density features) are extracted to describe the characteristics of clusters, in which the feature similarity is calculated to find the optimal matching relationship between different clusters in successive snapshots. Second, the feature similarity threshold is defined to distinguish the birth and death behaviors of dynamic clusters. In addition, a novel clustering validation index is proposed to evaluate the accuracy of clustering tracking results in time-varying channels. Finally, the measured and simulated channels at 28 GHz are used to validate the performance of the proposed algorithm. Numerical simulation results are provided to demonstrate the effectiveness and accuracy of the proposed algorithm. The proposed algorithm can accurately capture the nonstationary time-varying properties in millimeter-wave (mmWave) channels, which is of great significance for the fifth-generation (5G) and the sixth-generation (6G) channel modeling.