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Abstract

A novel scatterer density-based predictive channel modeling method includes: obtaining channel data with different scenarios scatterer densities through a channel measurement or a simulation; obtaining corresponding channel statistical characteristic parameters through a data preprocessing based on the channel data; constructing a graph dataset by taking scatterer density in different scenarios as main characteristics to enhance a space-time correlation of data; dividing the graph dataset according to a certain proportion, and then using a graph attention network and a gated recurrent unit network to extract correlated channel space-time characteristics and implementing a cross scenario channel prediction. The method can capture channel variations in different scenarios, and obtain channel characteristics under different scatterer densities through high space-time correlated channel characteristics, and has good performance in channel prediction based on scenario.

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