A 3-D Non-Stationary Model for Beyond 5G and 6G Vehicle-to-Vehicle mmWave Massive MIMO Channels

This paper proposes a novel three-dimensional (3D) non-stationary irregular-shaped geometry-based stochastic model (IS-GBSM) for beyond fifth-generation (B5G) and sixth-generation (6G) vehicle-to-vehicle (V2V) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. By distinguishing dynamic clusters and static clusters, the proposed IS-GBSM for the first time explores the impact of vehicular traffic density (VTD) on channel statistics in B5G/6G V2V mmWave massive MIMO scenarios. Furthermore, a novel method is developed to model the channel space-time-frequency non-stationarity. In the developed method, dynamic/static correlated clusters are first generated by an improved K-Means clustering algorithm. Then, by employing a birth-death process based on correlated clusters, the consistency in birth and death between dynamic/static correlated clusters during time-array evolution is modeled. Key channel statistical properties, e.g., space-time-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD), are derived. Simulation results demonstrate that the space-time-frequency non-stationarity is captured and the influence of VTDs on channel statistics is explored. Finally, the close agreement is achieved between simulation results and measurements, verifying the utility of proposed IS-GBSM.