Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive
radio and communication network. However, its application to the physical layer
is hampered by sophisticated channel environments and limited learning ability
of conventional ML algorithms. Deep learning (DL) has been recently applied for
many fields, such as computer vision and natural language processing, given its
expressive capacity and convenient optimization capability. The potential
application of DL to the physical layer has also been increasingly recognized
because of the new features for future communications, such as complex
scenarios with unknown channel models, high speed and accurate processing
requirements; these features challenge conventional communication theories.
This paper presents a comprehensive overview of the emerging studies on
DL-based physical layer processing, including leveraging DL to redesign a
module of the conventional communication system (for modulation recognition,
channel decoding, and detection) and replace the communication system with a
radically new architecture based on an autoencoder. These DL-based methods show
promising performance improvements but have certain limitations, such as lack
of solid analytical tools and use of architectures that are specifically
designed for communication and implementation research, thereby motivating
future research in this field.

介绍了基于深度学习的物理层处理的新兴研究，包括基于深度学习重新设计传统通信系统的模块（用于调制识别，信道译码和检测）并将通信系统替换为基于自动编码器的全新架构。