  Deep learning (DL) approaches have demonstrated high performance in
compressing and reconstructing the channel state information (CSI) and reducing
the CSI feedback overhead in massive MIMO systems. One key challenge, however,
with the DL approaches is the demand for extensive training data. Collecting
this real-world CSI data incurs significant overhead that hinders the DL
approaches from scaling to a large number of communication sites. To address
this challenge, we propose a novel direction that utilizes site-specific
\textit{digital twins} to aid the training of DL models. The proposed digital
twin approach generates site-specific synthetic CSI data from the EM 3D model
and ray tracing, which can then be used to train the DL model without
real-world data collection. To further improve the performance, we adopt online
data selection to refine the DL model training with a small real-world CSI
dataset. Results show that a DL model trained solely on the digital twin data
can achieve high performance when tested in a real-world deployment. Further,
leveraging domain adaptation techniques, the proposed approach requires orders
of magnitude less real-world data to approach the same performance of the model
trained completely on a real-world CSI dataset.
