  Accurate channel state information (CSI) is essential for downlink precoding
in frequency division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However,
obtaining CSI through feedback from the user equipment (UE) becomes challenging
with the increasing scale of antennas and subcarriers and leads to extremely
high CSI feedback overhead. Deep learning-based methods have emerged for
compressing CSI but these methods generally require substantial collected
samples and thus pose practical challenges. Moreover, existing deep learning
methods also suffer from dramatically growing feedback overhead owing to their
focus on full-dimensional CSI feedback. To address these issues, we propose a
low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework
(IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for
eigenvector-based CSI feedback is proposed to reduce the feedback overhead.
Then, to alleviate the necessity of extensive collected samples and enable
few-shot CSI feedback, we further propose a knowledge-driven data augmentation
(KDDA) method and an artificial intelligence-generated content (AIGC) -based
data augmentation method by exploiting the domain knowledge of wireless
channels and by exploiting a novel generative model, respectively. Experimental
results based on the DeepMIMO dataset demonstrate that the proposed IEFSF
significantly reduces CSI feedback overhead by 64 times compared with existing
methods while maintaining higher feedback accuracy using only several hundred
collected samples.
