  Accurate and effective channel state information (CSI) feedback is a key
technology for massive multiple-input and multiple-output systems. Recently,
deep learning (DL) has been introduced for CSI feedback enhancement through
massive collected training data and lengthy training time, which is quite
costly and impractical for realistic deployment. In this article, a
knowledge-driven meta-learning approach is proposed, where the DL model
initialized by the meta model obtained from meta training phase is able to
achieve rapid convergence when facing a new scenario during target retraining
phase. Specifically, instead of training with massive data collected from
various scenarios, the meta task environment is constructed based on the
intrinsic knowledge of spatial-frequency characteristics of CSI for meta
training. Moreover, the target task dataset is also augmented by exploiting the
knowledge of statistical characteristics of wireless channel, so that the DL
model can achieve higher performance with small actually collected dataset and
short training time. In addition, we provide analyses of rationale for the
improvement yielded by the knowledge in both phases. Simulation results
demonstrate the superiority of the proposed approach from the perspective of
feedback performance and convergence speed.
