A Simultaneous Clustering and Tracking Algorithm for Capturing Cluster-Level Spatial Consistency in 6G Wireless Channels

Jiaxin Lin, Pan Tang, Jianhua Zhang, Zhaowei Chang, Peijie Liu, Yufeng Qin, Ke Chen, Huixin Xu, Byonghyo Shim

Spatial consistency is a fundamental physical property of wireless channels that reflects the smooth evolution of the channel between spatial locations. At the cluster level, it requires similar multipath components (MPCs) remain grouped into the same clusters as the transceivers move, enabling consistent cluster tracking. Cluster-level spatial consistency is essential for realistic cluster-based channel models, especially for potential 6G techniques such as massive MIMO, integrated sensing and communication, and terahertz (THz) communication. However, existing clustering and tracking methods do not fully exploit spatial correlations of MPCs. In tracking-after-clustering, clustering and tracking are decoupled, while joint clustering-and-tracking mainly relies on cluster centers from the previous snapshot. In this work, we propose a Mahalanobis-distance-based simultaneous clustering and tracking (MD-SCT) algorithm to capture the joint distribution of clustered MPCs in the delay, angular and spatial domains. Under Mahalanobis distance, MPCs in successive snapshots are associated with existing clusters, thereby inherently tracking while clustering. The algorithm is further applied in the sub-THz band. Performance is evaluated using mean square successive difference and gradient change rate. The results demonstrate that the proposed algorithm yields smoother cluster evolution. This improves the reliability of clustered channels for spatial consistency modeling in 6G.