  Improving learning efficiency is paramount for learning resource allocation
with deep neural networks (DNNs) in wireless communications over highly dynamic
environments. Incorporating domain knowledge into learning is a promising way
of dealing with this issue, which is an emerging topic in the wireless
community. In this article, we first briefly summarize two classes of
approaches to using domain knowledge: introducing mathematical models or prior
knowledge to deep learning. Then, we consider a kind of symmetric prior,
permutation equivariance, which widely exists in wireless tasks. To explain how
such a generic prior is harnessed to improve learning efficiency, we resort to
ranking, which jointly sorts the input and output of a DNN. We use power
allocation among subcarriers, probabilistic content caching, and interference
coordination to illustrate the improvement of learning efficiency by exploiting
the property. From the case study, we find that the required training samples
to achieve given system performance decreases with the number of subcarriers or
contents, owing to an interesting phenomenon: "sample hardening". Simulation
results show that the training samples, the free parameters in DNNs and the
training time can be reduced dramatically by harnessing the prior knowledge.
The samples required to train a DNN after ranking can be reduced by $15 \sim
2,400$ folds to achieve the same system performance as the counterpart without
using prior.
