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摘要翻译：
随着技术的发展和数字电视的普及，许多研究者都在研究更高效的传输和接收方法。这一事实的发生是因为使用新标准，如8K超级高视图，以更好的质量传输视频的需求。在这种情况下，滤波器组多载波等调制技术，结合先进的编码和同步方法，正在被应用，旨在实现所需的数据速率，以支持超高清视频。同时，研究能够更好地接收发射信号的信道估计方法也很重要。这项任务并不总是微不足道的，这取决于通道的特性。因此，使用人工智能可以有助于从发射的导频估计信道频率响应。一种经典的反向传播训练算法可以用来寻找信道均衡器系数，从而使电视信号的正确接收成为可能。因此，本文提出了一种利用神经网络技术来获得巴西数字电视系统ISDB-TB中的信道响应的方法，该方法使用滤波器组多载波。
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英文标题：
《An Application of Neural Networks to Channel Estimation of the ISDB-TB
FBMC System》
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作者：
Jefferson Jesus Hengles Almeida, P. B. Lopes, Cristiano Akamine, and
Nizam Omar
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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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英文摘要：
Due to the evolution of technology and the diffusion of digital television, many researchers are studying more efficient transmission and reception methods. This fact occurs because of the demand of transmitting videos with better quality using new standards such 8K SUPER Hi-VISION. In this scenario, modulation techniques such as Filter Bank Multi Carrier, associated with advanced coding and synchronization methods, are being applied, aiming to achieve the desired data rate to support ultra-high definition videos. Simultaneously, it is also important to investigate ways of channel estimation that enable a better reception of the transmitted signal. This task is not always trivial, depending on the characteristics of the channel. Thus, the use of artificial intelligence can contribute to estimate the channel frequency response, from the transmitted pilots. A classical algorithm called Back-propagation Training can be applied to find the channel equalizer coefficients, making possible the correct reception of TV signals. Therefore, this work presents a method of channel estimation that uses neural network techniques to obtain the channel response in the Brazilian Digital System Television, called ISDB-TB, using Filter Bank Multi Carrier.
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PDF链接：
https://arxiv.org/pdf/1803.01141
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