An Application of Neural Networks to Channel Estimation of the ISDB-TB FBMC System

Jefferson Jesus Hengles Almeida, P. B. Lopes, C. Akamine, Nizam Omar
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引用次数: 5

Abstract

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.
神经网络在ISDB-TB FBMC系统信道估计中的应用
由于技术的发展和数字电视的普及,许多研究者正在研究更有效的传输和接收方法。这是因为使用8K超高清(SUPER Hi-VISION)等新标准传输质量更好的视频的需求。在这种情况下,正在应用滤波器组多载波等调制技术,结合先进的编码和同步方法,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传输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,使用人工智能可以从传输的导频中估计信道频率响应。一种称为反向传播训练的经典算法可以用来找到信道均衡器系数,使电视信号的正确接收成为可能。因此,这项工作提出了一种信道估计方法,该方法使用神经网络技术获得巴西数字系统电视(称为ISDB-TB)中的信道响应,使用滤波器组多载波。
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