  Compressive sensing is a promising solution for the channel estimation in
multiple-input multiple-output (MIMO) systems with large antenna arrays and
constrained hardware. Utilizing site-specific channel data from real-world
systems, deep learning can be employed to learn the compressive sensing
measurement vectors with minimum redundancy, thereby focusing sensing power on
promising spatial directions of the channel. Collecting real-world channel
data, however, is challenging due to the high overhead resulting from the large
number of antennas and hardware constraints. In this paper, we propose
leveraging a site-specific digital twin to generate synthetic channel data,
which shares a similar distribution with real-world data. The synthetic data is
then used to train the deep learning models for learning measurement vectors
and hybrid precoder/combiner design in an end-to-end manner. We further propose
a model refinement approach to fine-tune the model pre-trained on the digital
twin data with a small amount of real-world data. The evaluation results show
that, by training the model on the digital twin data, the learned measurement
vectors can be efficiently adapted to the environment geometry, leading to high
performance of hybrid precoding for real-world deployments. Moreover, the model
refinement approach can enable the digital twin aided model to achieve
comparable performance to the model trained on the real-world dataset with a
significantly reduced amount of real-world data.
