Abstract: Based on the Bird’s Nest 28 GHz channel measurement data, this paper proposes a spatially consistent channel large-scale parameter generation algorithm based on spatial recursion, and compares it with the traditional WINNER II algorithm. This algorithm uses multi-dimensional normal distribution and specific recursion algorithm and generates the channel large-scale parameters of each simulation point sequentially, to realize the auto-correlation and cross-correlation characteristics of the parameters. The results show that, on the premise of achieving spatial consistency, the simulation results of the spatial recursive parameter generation algorithm are highly consistent with the measured data, the simulation accuracy of this algorithm is better than the WINNER II algorithm. Compared with the WINNER II algorithm, the spatial recursive parameter generation algorithm does not need to generate the parameter cross-correlation characteristics separately, which simplifies the simulation steps. The spatial recursive parameter generation algorithm provides a way for the research of related algorithms. This new idea is of great significance for millimetre-wave time-varying channel simulation.