The invention discloses a reconfigurable intelligent surface (RIS)-assisted multiple-input-multiple-output (
MIMO) implicit channel
estimation method based on a graph
attention network, which is used for efficient
downlink transmission in a multi-user scene. Firstly, a graph
attention network is designed, user nodes and RIS nodes are modeled in a unified mode, received
pilot signals serve as initial features, spatial feature expression is enhanced in combination with user three-dimensional position information, and therefore interference between users and a
spatial correlation structure are accurately represented; secondly, end-to-end
feature aggregation is achieved based on a
message passing mechanism, a
base station beam forming matrix and an RIS
reflection coefficient are directly predicted under the condition that explicit channel
estimation is not needed, and the total transmitting power constraint and the unit mode constraint are met through normalization
processing so as to complete joint optimization; according to the method, the users and the speed of the
system can be remarkably improved under limited
pilot frequency overhead, and the method has excellent generalization performance and robustness in a multi-user complex propagation environment.