  In massive multiple-input multiple-output (MIMO) systems under the frequency
division duplexing (FDD) mode, the user equipment (UE) needs to feed channel
state information (CSI) back to the base station (BS). Though deep learning
approaches have made a hit in the CSI feedback problem, whether they can remain
excellent in actual environments needs to be further investigated. In this
letter, we point out that the real-time dataset in application often has the
domain gap from the training dataset caused by the time delay. To bridge the
gap, we propose bubble-shift (B-S) data augmentation, which attempts to offset
performance degradation by changing the delay and remaining the channel
information as much as possible. Moreover, random-generation (R-G) data
augmentation is especially proposed for outdoor scenarios due to the complex
distribution of its channels. It generalizes the characteristics of the channel
matrix and alleviates the over-fitting problem. Simulation results show that
the proposed data augmentation boosts the robustness of networks in both indoor
and outdoor environments. The open source codes are available at
https://github.com/zhanghy23/CRNet-Aug.
