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.

翻译：压缩感知是大规模天线阵列且硬件受限的多输入多输出（MIMO）系统中信道估计的一种有前景的解决方案。利用来自真实系统的站点专用信道数据，可以采用深度学习以最小冗余学习压缩感知测量向量，从而将感知能力聚焦于信道的潜在空间方向。然而，由于大规模天线及硬件限制导致的高开销，收集真实信道数据具有挑战性。本文提出利用站点专用数字孪生生成合成信道数据，该数据与真实数据具有相似分布。随后，利用合成数据以端到端方式训练深度学习模型，用于学习测量向量及混合预编码器/组合器设计。我们进一步提出一种模型精化方法，利用少量真实数据对预训练于数字孪生数据的模型进行微调。评估结果表明，通过在数字孪生数据上训练模型，学习到的测量向量能够高效适应环境几何特征，从而在真实部署中实现混合预编码的高性能。此外，模型精化方法可使数字孪生辅助模型在显著减少真实数据量的情况下，达到与在真实数据集上训练的模型相当的性能。