  Deep learning (DL)-based channel state information (CSI) feedback has shown
promising potential to improve spectrum efficiency in massive MIMO systems.
However, practical DL approaches require a sizeable CSI dataset for each
scenario, and require large storage or updating bandwidth for multiple learned
models. To overcome this costly barrier, we develop a solution for efficient
training and deployment enhancement of DL-based CSI feedback by exploiting a
lightweight translation model to cope with new CSI environments and by
proposing novel dataset augmentation based on domain knowledge. Specifically,
we first develop a deep unfolding CSI feedback network, SPTM2-ISTANet+, which
employs spherical normalization to address the challenge of path loss
variation. We also introduce an integration of a trainable measurement matrix
and residual CSI recovery blocks within SPTM2-ISTANet+ to improve efficiency
and accuracy. Using SPTM2-ISTANet+ as the anchor feedback model, we propose an
efficient scenario-adaptive CSI feedback architecture. This new CSI-TransNet
exploits a plug-in module for CSI translation consisting of a sparsity aligning
function and lightweight DL module to reuse pretrained models in unseen
environments. To work with small datasets, we propose a lightweight and general
augmentation strategy based on domain knowledge. Test results demonstrate the
efficacy and efficiency of the proposed solution for accurate CSI feedback
given limited measurements for unseen CSI environments.
