  Deep learning (DL), a branch of artificial intelligence (AI) techniques, has
shown great promise in various disciplines such as image classification and
segmentation, speech recognition, language translation, among others. This
remarkable success of DL has stimulated increasing interest in applying this
paradigm to wireless channel estimation in recent years. Since DL principles
are inductive in nature and distinct from the conventional rule-based
algorithms, when one tries to use DL technique to the channel estimation, one
might easily get stuck and confused by so many knobs to control and small
details to be aware of. The primary purpose of this paper is to discuss key
issues and possible solutions in DL-based wireless channel estimation and
channel state information (CSI) feedback including the DL model selection,
training data acquisition, and neural network design for 6G. Specifically, we
present several case studies together with the numerical experiments to
demonstrate the effectiveness of the DL-based wireless channel estimation
framework.
