摘要翻译：
最近，深度学习方法在通信系统中显示出显著的改进。本文研究了非线性信道下的神经网络均衡问题。提出了一种基于神经网络的联合均衡器和译码器，实现了不需要信道状态信息(CSI)的盲均衡和盲译码。与以往的方法不同，我们使用两个神经网络而不是一个。首先，利用卷积神经网络(CNN)自适应地从信道损伤和非线性失真中恢复传输信号。然后深度神经网络解码器(NND)对来自CNN均衡器的检测信号进行解码。实验结果表明，在不同信道条件下，本文提出的CNN均衡器比其他基于机器学习方法的均衡器取得了更好的性能。与最先进的模型相比，所提出的模型减少了约2/3美元的参数。此外，我们的模型可以很容易地应用于复杂度为$\mathcal{O}(n)$的长序列。
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英文标题：
《Joint Neural Network Equalizer and Decoder》
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作者：
Weihong Xu (1 and 2 and 3), Zhiwei Zhong (1 and 2 and 3), Yair Be'ery
  (4), Xiaohu You (1 and 2 and 3), Chuan Zhang (1 and 2 and 3) ((1) Lab of
  Efficient Architectures for Digital-communication and Signal-processing
  (LEADS), (2) National Mobile Communications Research Laboratory, (3) Quantum
  Information Center, Southeast University, China, (4) School of Electrical
  Engineering, Tel-Aviv University, Israel)
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最新提交年份：
2018
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分类信息：

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：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：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：
  Recently, deep learning methods have shown significant improvements in communication systems. In this paper, we study the equalization problem over the nonlinear channel using neural networks. The joint equalizer and decoder based on neural networks are proposed to realize blind equalization and decoding process without the knowledge of channel state information (CSI). Different from previous methods, we use two neural networks instead of one. First, convolutional neural network (CNN) is used to adaptively recover the transmitted signal from channel impairment and nonlinear distortions. Then the deep neural network decoder (NND) decodes the detected signal from CNN equalizer. Under various channel conditions, the experiment results demonstrate that the proposed CNN equalizer achieves better performance than other solutions based on machine learning methods. The proposed model reduces about $2/3$ of the parameters compared to state-of-the-art counterparts. Besides, our model can be easily applied to long sequence with $\mathcal{O}(n)$ complexity.
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PDF链接：
https://arxiv.org/pdf/1807.0204