In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.

中文翻译：

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OFDM系统信道估计的投影小波加权孪生支持向量回归

本文提出了一种基于正交频分复用系统(OFDM)系统信道估计算法的高效投影小波加权孪生支持向量回归(PWWTSVR)算法。大多数OFDM系统的信道估计算法都是基于信道模型的线性假设。在所提出的算法中，OFDM系统信道在时域和频域都被消耗为非线性和衰落。PWWTSVR利用导频信号估计非线性无线信道的响应，这是SVR的主要工作领域。由于利用训练数据的先验信息，PWWRSVR 的最优目标函数中的投影轴旨在最小化投影点的方差。与传统的支持向量回归算法不同，所提出的 PWWTSVR 模型中不同位置的训练样本被赋予由小波变换确定的不同惩罚权重。权重应用于二次经验风险项和一阶经验风险项，以减少异常值的影响。最终的回归量可以在一定程度上避免过拟合问题，并对信道估计产生很强的泛化能力。数值实验结果表明，与传统的导频辅助信道估计方法相比，该算法具有更好的性能。最终的回归量可以在一定程度上避免过拟合问题，并对信道估计产生很强的泛化能力。数值实验结果表明，与传统的导频辅助信道估计方法相比，该算法具有更好的性能。最终的回归量可以在一定程度上避免过拟合问题，并对信道估计产生很强的泛化能力。数值实验结果表明，与传统的导频辅助信道估计方法相比，该算法具有更好的性能。