Clustering Performance Evaluation Algorithm for Vehicle-to-Vehicle Radio Channels

Nowadays, propagation channels are mostly modeled based on the structure of the clusters of multipath components (MPCs). The clusters of the MPCs can be identified by using clustering algorithms. Most of the clustering algorithms are sensitive to the number of clusters, which however, is hard to be acquired from the channel measurement. In this case, most of the algorithms use clustering performance evaluating methods to determine the best number of clusters, e.g., Calinski-Harabasz (CH) Index and Xie-Beni (XB) Index. Nevertheless, none of the current evaluating methods are able to properly evaluate the time-varying clustering results, where the evolution pattern in time dimension needs to be additionally considered. In this paper, we propose a novel clustering evaluation method for the clusters in time-varying channels, which considers the evolution pattern of the clusters during the evaluation. Based on the simulations, the new cluster evaluation can better assess the time-varying channels comparing to the existing evaluation methods.