  Deep learning (DL) has achieved great success in signal processing and
communications and has become a promising technology for future wireless
communications. Existing works mainly focus on exploiting DL to improve the
performance of communication systems. However, the high memory requirement and
computational complexity constitute a major hurdle for the practical deployment
of DL-based communications. In this article, we investigate how to compress and
accelerate the neural networks (NNs) in communication systems. After
introducing the deployment challenges for DL-based communication algorithms, we
discuss some representative NN compression and acceleration techniques.
Afterwards, two case studies for multiple-input-multiple-output (MIMO)
communications, including DL-based channel state information feedback and
signal detection, are presented to show the feasibility and potential of these
techniques. We finally identify some challenges on NN compression and
acceleration in DL-based communications and provide a guideline for subsequent
research.
