  The AI-enabled autoencoder has demonstrated great potential in channel state
information (CSI) feedback in frequency division duplex (FDD) multiple input
multiple output (MIMO) systems. However, this method completely changes the
existing feedback strategies, making it impractical to deploy in recent years.
To address this issue, this paper proposes a channel modeling aided data
augmentation method based on a limited number of field channel data.
Specifically, the user equipment (UE) extracts the primary stochastic
parameters of the field channel data and transmits them to the base station
(BS). The BS then updates the typical TR 38.901 model parameters with the
extracted parameters. In this way, the updated channel model is used to
generate the dataset. This strategy comprehensively considers the dataset
collection, model generalization, model monitoring, and so on. Simulations
verify that our proposed strategy can significantly improve performance
compared to the benchmarks.
