A 3D Non-Stationary Small-Scale Fading Model for 5G High-Speed Train Massive MIMO Channels

The use of fifth-generation (5G) communication technology by high-speed trains (HSTs) has a lot of potential to satisfy current needs for high data rates. Therefore, accurate modeling of the HST wireless channels is crucial for the design and performance assessment of the 5G systems. This paper proposes a general three-dimensional (3D) non-stationary small-scale fading model for 5G HST millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels, which captures the wireless channel characteristics in different HST operating scenarios. The proposed channel model has two characteristics. Firstly, it incorporates the distribution of overhead line poles along the railway to characterize the periodic scattering components from the poles. Secondly, it represents complex HST operating scenarios as combinations of five types of scattering clusters, namely hills, trees, lakes, buildings, and concrete. Furthermore, the impact of environmental complexity (EC) on channel statistical properties in the 5G HST massive MIMO scenarios is investigated. Afterwards, based on the birth-death process of scattering clusters, the proposed channel model can characterize the channel non-stationarity in the space-time-frequency domain. Simulation results demonstrate that the proposed model effectively captures the channel non-stationarity, the scattering characteristics of overhead line poles, and the impact of different ECs on system performance. The accuracy and practicality of the proposed model are validated through a comparison with measurement results.