  Recently, deep neural network (DNN)-based physical layer communication
techniques have attracted considerable interest. Although their potential to
enhance communication systems and superb performance have been validated by
simulation experiments, little attention has been paid to the theoretical
analysis. Specifically, most studies in the physical layer have tended to focus
on the application of DNN models to wireless communication problems but not to
theoretically understand how does a DNN work in a communication system. In this
paper, we aim to quantitatively analyze why DNNs can achieve comparable
performance in the physical layer comparing with traditional techniques, and
also drive their cost in terms of computational complexity. To achieve this
goal, we first analyze the encoding performance of a DNN-based transmitter and
compare it to a traditional one. And then, we theoretically analyze the
performance of DNN-based estimator and compare it with traditional estimators.
Third, we investigate and validate how information is flown in a DNN-based
communication system under the information theoretic concepts. Our analysis
develops a concise way to open the "black box" of DNNs in physical layer
communication, which can be applied to support the design of DNN-based
intelligent communication techniques and help to provide explainable
performance assessment.
