发帖
雷达卡
摘要翻译：
深度学习已经成为通信系统和各个领域的一项开创性技术。尽管近年来基于深度神经网络(DNN)的技术取得了显著的进展，但在要求实时性的实际通信系统中，DNN的高计算复杂度一直是DNN应用的主要障碍。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。本文首次提出了一种有效的学习结构和设计策略，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，以缓解这一挑战，并推断DNN在通信系统中的可行性和潜力。特别地，DNN应用于编码器和解码器，以使得能够相对于系统环境灵活地自适应，而不需要任何域特定信息。作者对基于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进行了广泛的研究和跨学科设计考虑，从而确定了基于DNN的通信在实践中的应用。
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英文标题：
《Building Encoder and Decoder with Deep Neural Networks: On the Way to
Reality》
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作者：
Minhoe Kim, Woonsup Lee, Jungmin Yoon, Ohyun Jo
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最新提交年份：
2018
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Information Theory 信息论
分类描述：Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料，并与H.1.1有交集。
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一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Mathematics 数学
二级分类：Information Theory 信息论
分类描述：math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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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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PDF链接：
https://arxiv.org/pdf/1808.02401
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