近年来,新兴出现的深度学习(Deep Learning, DL)已经被认为是一种能很好地处理复杂通信问题的技术,基于DL的算法可以大大提高通信系统的性能。本文概述了DL算法在通信物理层应用的最新进展,首先回顾了DL在信道估计、均衡、信号检测、信道状态信息(Channel State Information,CSI)反馈等物理层方面的代表性应用,然后讨论了DL在通信物理层应用的挑战。DL在物理层的应用将为无线通信的研究带来新的方向。
In recent years, the emerging Deep Learning (DL) has been considered as a technology that can handle complex communication problems well. DL-based algorithms can greatly improve the performance of communication systems. This paper provides an overview of the recent advancements in DL-based physical layer communications. First, the typical application of DL in the physical layer such as channel estimation, equalization, signal detection, and channel state information (CSI) feedback, etc. are reviewed, and then the opportunities and challenges of DL application in communication physical layer are discussed. The application of DL in physical layer will bring new directions to research of wireless communication.
Information & Communications