Graph‐based stochastic model for high‐speed railway cutting scenarios

Abstract

This study proposes a stochastic channel model based on the propagation graph theory for high-speed railway (HSR) cutting scenarios. Single-input single-output wideband measurements are conducted under a cutting for acquiring realistic channel data. The propagation graph covering line of sight, single bounced and double bounced conditions is used to describe the measured propagation scenario and generate the virtual channel impulse responses. The graph model is validated focusing on small-scale characteristics such as Ricean K-factor, delay and frequency dispersions, and a close agreement is achieved between model and measurement results. In particular, the verified model is extended to the multiple-input multiple-output case for simulation analysis of MIMO performance of the measured environment, concentrating on the spatial correlation and the ergodic capacity. The proposed model can facilitate the reliable simulation and evaluation of MIMO systems in the HSR cutting scenario.