用深度神经网络构建编码器和解码器：在实现的路上
Building Encoder and Decoder with Deep Neural Networks: On the Way to Reality
摘要：
深度学习已经成为各个领域以及通信系统中的突破性技术。尽管近年来基于深度神经网络（DNN）的技术取得了显著进步，但高计算复杂性一直是将DNN应用于需要实时操作的实际通信系统的主要障碍。从这个意义上说，在基于DNN的智能通信的扩散成为现实之前，必须解决实际实施方面的挑战。据作者所知，本文首次提出了一种有效的学习架构和设计策略，包括使用硬件描述语言（HDL）通过数字电路实现的链路级验证，以减轻这一挑战，并推断DNN在通信系统中的可行性和潜力。具体地，DNN被应用于编码器和解码器，以使得能够在不需要任何域特定信息的情况下相对于系统环境进行灵活的自适应。作者考虑了广泛的调查和跨学科的设计考虑，包括基于DNN的自动编码器结构，学习框架和用于实时操作的低复杂度数字电路实现，从而确保了基于DNN的通信在实践中的使用。
英文摘要：
Deep learning has been a groundbreaking technology in various fields as well as in communications systems. In spite of the notable advancements of deep neural network (DNN) based technologies in recent years, the high computational complexity has been a major obstacle to apply DNN in practical communications systems which require real-time operation. In this sense, challenges regarding practical implementation must be addressed before the proliferation of DNN-based intelligent communications becomes a reality. To the best of the authors' knowledge, for the first time, this article presents an efficient learning architecture and design strategies including link level verification through digital circuit implementations using hardware description language (HDL) to mitigate this challenge and to deduce feasibility and potential of DNN for communications systems. In particular, DNN is applied for an encoder and a decoder to enable flexible adaptation with respect to the system environments without needing any domain specific information. Extensive investigations and interdisciplinary design considerations including the DNN-based autoencoder structure, learning framework, and low-complexity digital circuit implementations for real-time operation are taken into account by the authors which ascertains the use of DNN-based communications in practice.
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