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.

中文翻译：

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用于通信的神经网络的压缩和加速

DL在信号处理和通信方面取得了巨大的成功，并已成为未来无线通信的有希望的技术。现有工作主要集中在利用DL来改善通信系统的性能。但是，高内存需求和计算复杂性是实际部署基于DL的通信的主要障碍。在本文中，我们研究了如何压缩和加速通信系统中的神经网络（NNs）。在介绍了基于DL的通信算法的部署挑战之后，我们讨论了一些代表性的NN压缩和加速技术。之后，针对多输入多输出（MIMO）通信的两个案例研究，包括基于DL的信道状态信息反馈和信号检测，展示这些技术的可行性和潜力。我们最终确定了基于DL的通信中的NN压缩和加速方面的一些挑战，并为后续研究提供了指南。