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arXiv 2106.01124eess.SPcs.ITcs.LGmath.IT
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在物理层通信中打开深度神经网络的黑匣子

Opening the Black Box of Deep Neural Networks in Physical Layer Communication

National University of Defense Technology（国防科技大学）

Jun Liu, Haitao Zhao, Dongtang Ma, Kai Mei, Jibo Wei

备注：
6 pages, 5 figures, to be presented in the IEEE Wireless Communications and Networking Conference (WCNC) 2022 Workshop on Machine Learning for Communications: Future Large Scale MIMO and AI-Native Air-Interface
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摘要：

基于深度神经网络（DNN）的物理层技术由于其增强通信系统的潜力而吸引了相当大的兴趣。然而，在物理层的大多数研究往往集中在应用DNN模型的无线通信的问题，而不是从理论上理解DNN如何在通信系统的工作。在本文中，我们的目标是定量分析为什么DNN可以实现相当的性能在物理层与传统技术相比，其成本方面的计算复杂度。我们进一步研究，并通过实验验证信息是如何在基于DNN的通信系统下的信息理论的概念。

英文摘要：

Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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