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首页> 外文期刊>International journal of intelligent engineering informatics >Analysis of enhanced complex SVR interpolation and SCG-based neural networks for LTE downlink system
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Analysis of enhanced complex SVR interpolation and SCG-based neural networks for LTE downlink system

机译：LTE下行系统的增强型复杂SVR插值和基于SCG的神经网络分析

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In this article, we operate and evaluate the performance of radial basis function (RBF)-based support vector machine regression (SVR) and scaled conjugate gradient back propagation (SCG)-based artificial neural network (ANN), to estimate the channel deviations in frequency domain using the standardised pilot symbols structure for LTE downlink system. We apply complex SVR and ANN to estimate the real vehicular a channel environment well-defined by the International Telecommunications Union (ITU). The suggested procedures use data obtained from the received pilot symbols to estimate the overall frequency response of the frequency selective multipath fading channel in two stages. In the first stage, each technique learns to adjust to the channel fluctuations, then, in the second stage, it predicts all the channel frequency responses. Lastly, in order to assess the abilities of the considered channel estimators, we deliver performance of complex SVR and ANN, which are compared to traditional least squares (LS) and decision feedback (DF) methods. Computer simulation results demonstrate that the complex RBF-based SVR approach has a better precision than other estimation methods.
机译：在本文中，我们操作和评估基于径向基函数（RBF）的支持向量机回归（SVR）和基于比例共轭梯度反向传播（SCG）的人工神经网络（ANN）的性能，以估算通道偏差LTE下行系统使用标准化导频符号结构的频域。我们应用复杂的SVR和ANN来估计由国际电信联盟（ITU）明确定义的真实车辆通道环境。建议的过程使用从接收到的导频符号中获得的数据来分两阶段估算频率选择多径衰落信道的整体频率响应。在第一阶段，每种技术都学会调整信道波动，然后在第二阶段，预测所有信道频率响应。最后，为了评估考虑的信道估计器的能力，我们提供了复杂的SVR和ANN的性能，并与传统的最小二乘（LS）和决策反馈（DF）方法进行了比较。计算机仿真结果表明，基于复杂RBF的SVR方法比其他估计方法具有更高的精度。

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