Fast time-varying FDD massive MIMO system with intelligent CSI feedback mechanism.

An intelligent CSI feedback method is suggested for the fast time frequency conversion division duplex (FDD) large-scale multiple-input multiple-output (MIMO) system in wireless channel interference because the channel state information (CSI) matrix has noise and the Doppler frequency shift-induced time correlation prevents the system from providing high-reliability and low-latency communication. In order to extract noise from the CSI matrix and discover the spatial organisation of the channel, this technique combines convolutional neural networks (CNN) and batch normalisation (BN) networks. To increase the accuracy of the CSI reconstruction, the time correlation between the CSI matrices is reconstructed using the attention approach. The network is trained offline using the results of the standard fast time-varying channel model simulation. System modelling plus analysis show that the proposed method can successfully extract the Doppler-induced time correlation and limit the impact of noise. The proposed method performs better in terms of normalised mean square error (NMSE) and cosine similarity when compared to the representative CSI compressed feedback method and CsiNet method. [ABSTRACT FROM AUTHOR]