  Accurate downlink channel state information (CSI) is vital to achieving high
spectrum efficiency in massive MIMO systems. Existing works on the deep
learning (DL) model for CSI feedback have shown efficient compression and
recovery in frequency division duplex (FDD) systems. However, practical DL
networks require sizeable wireless CSI datasets during training to achieve high
model accuracy. To address this labor-intensive problem, this work develops an
efficient training enhancement solution of DL-based feedback architecture based
on a modest dataset by exploiting the complex CSI features, and augmenting CSI
dataset based on domain knowledge. We first propose a spherical CSI feedback
network, SPTM2-ISTANet+, which employs the spherical normalization framework to
mitigate the effect of path loss variation. We exploit the trainable
measurement matrix and residual recovery structure to improve the encoding
efficiency and recovery accuracy. For limited CSI measurements, we propose a
model-driven lightweight and universal augmentation strategy based on
decoupling CSI magnitude and phase information, applying the circular shift in
angular-delay domain, and randomizing the CSI phase to approximate phase
distribution. Test results demonstrate the efficacy and efficiency of the
proposed training strategy and feedback architecture for accurate CSI feedback
under limited measurements.
