  Deep learning is a potential paradigm changer for the design of wireless
communications systems (WCS), from conventional handcrafted schemes based on
sophisticated mathematical models with assumptions to autonomous schemes based
on the end-to-end deep learning using a large number of data. In this article,
we present a basic concept of the deep learning and its application to WCS by
investigating the resource allocation (RA) scheme based on a deep neural
network (DNN) where multiple goals with various constraints can be satisfied
through the end-to-end deep learning. Especially, the optimality and
feasibility of the DNN based RA are verified through simulation. Then, we
discuss the technical challenges regarding the application of deep learning in
WCS.
