A Cluster-Based Channel Model Incorporating Quasi-Stationary Segmentation for Vehicle-to-Vehicle Communications

Abstract

Vehicle-to-vehicle (V2V) wireless communication is vital for intelligent transportation systems (ITSs). The high mobility of transceivers, along with the complex 3D propagation caused by low antenna heights and short communication ranges, present challenges to propagation modeling. Accurate V2V channel models are crucial for capturing these characteristics to design reliable V2V systems. Existing cluster-based V2V channel models neglect Doppler frequency variations in cluster classification, reducing classification and model accuracy. They describe clusters in single snapshot, missing temporal channel stationarity, and their complex structures slow model generation, hampering ITS applications. This paper presents a cluster-based V2V channel model incorporating quasi-stationary segmentation. First, SAGE algorithm extracts Multipath components (MPCs), followed by clustering and tracking. By analyzing clusters' Doppler frequency variations alongside angle, delay, and power changes, clusters are more accurately classified into global, static and dynamic types. Next, the model uses Correlation matrix distances (CMDs) to perform quasi-stationary segments for each cluster type, characterizing their distributions within each segment via inter- and intra-cluster parameters. This simplifies the model structure compared to single-snapshot models, improving generation efficiency. Segment duration and quantity statistics characterize channel stationarity. The model is validated by comparing simulated second-order channel statistics with comparable models and measured data. Its complexity is evaluated by comparing model generation time with alternative models in the literature.