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.

本文研究深度神经网络在物理层通信中的应用，首先通过比较传统和基于 DNN 的编码器的性能来验证其可行性，接着通过理论分析 DNN 估计器的性能来展示其吸引人的特点，并探究 DNN 通信系统中的信息流动机制以及与传统的通信系统的差异。该研究为 DNN 智能通信技术的设计提供支持并提供可解释的性能评估。