Deep learning for wireless physical layer: Opportunities and challenges

@article{Wang2017DeepLF,
  title={Deep learning for wireless physical layer: Opportunities and challenges},
  author={Tianqi Wang and Chao-Kai Wen and Hanqing Wang and Feifei Gao and Tao Jiang and Shi Jin},
  journal={China Communications},
  year={2017},
  volume={14},
  pages={92-111}
}
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… CONTINUE READING