  Deep learning has been a groundbreaking technology in various fields as well
as in communications systems. In spite of the notable advancements of deep
neural network (DNN) based technologies in recent years, the high computational
complexity has been a major obstacle to apply DNN in practical communications
systems which require real-time operation. In this sense, challenges regarding
practical implementation must be addressed before the proliferation of
DNN-based intelligent communications becomes a reality. To the best of the
authors' knowledge, for the first time, this article presents an efficient
learning architecture and design strategies including link level verification
through digital circuit implementations using hardware description language
(HDL) to mitigate this challenge and to deduce feasibility and potential of DNN
for communications systems. In particular, DNN is applied for an encoder and a
decoder to enable flexible adaptation with respect to the system environments
without needing any domain specific information. Extensive investigations and
interdisciplinary design considerations including the DNN-based autoencoder
structure, learning framework, and low-complexity digital circuit
implementations for real-time operation are taken into account by the authors
which ascertains the use of DNN-based communications in practice.
