Intelligent communication is gradually considered as the mainstream direction
in future wireless communications. As a major branch of machine learning, deep
learning (DL) has been applied in physical layer communications and has
demonstrated an impressive performance improvement in recent years. However,
most of the existing works related to DL focus on data-driven approaches, which
consider the communication system as a black box and train it by using a huge
volume of data. Training a network requires sufficient computing resources and
extensive time, both of which are rarely found in communication devices. By
contrast, model-driven DL approaches combine communication domain knowledge
with DL to reduce the demand for computing resources and training time. This
article reviews the recent advancements in the application of model-driven DL
approaches in physical layer communications, including transmission scheme,
receiver design, and channel information recovery. Several open issues for
further research are also highlighted after presenting the comprehensive
survey.

该文章回顾了最近在物理层通信中使用模型驱动深度学习方法的应用，包括传输方案、接收机设计和信道信息恢复，并在展示全面调查后，提出了若干进一步研究的开放问题。

基于模型的深度学习在物理层通信中的应用

Model-Driven Deep Learning for Physical Layer Communications

Deep learning (DL) has shown the great potentials to break the bottleneck of
communication systems. This article provides an overview on the recent
advancements in DL-based physical layer communications. DL can improve the
performance of each individual block in communication systems or optimize the
whole transmitter/receiver. Therefore, we categorize the applications of DL in
physical layer communications into systems with and without block structures.
For DL-based communication systems with block structures, we demonstrate the
power of DL in signal compression and signal detection. We also discuss the
recent endeavors in developing end-to-end communication systems. Finally, the
potential research directions are identified to boost the intelligent physical
layer communications with DL.

本文概述了基于深度学习的物理层通信的最新进展，将 DL 在通信系统中的应用分为具有块结构和没有块结构的系统，探讨了 DL 在信号压缩和信号检测中的应用，以及最近为开发端到端通信系统而做的努力，并指出了提升具有 DL 的智能物理层通信的潜在研究方向。