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Channel Estimation Using a Chirp Signal and the Fractional Fourier Transform

机译:线性调频信号和分数阶傅里叶变换的信道估计

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The Fractional Fourier Transform (FrFT) is a useful tool that has many applications, such as interference mitigation for communications and radar target echo separation. In this paper, we present a new use, which is estimating an unknown multipath channel, by sending a short chirp signal through the channel. The multiple received chirps in multipath are rotated to the proper FrFT dimension where they become high power tones, whose amplitudes and delays are easily estimated by determining which values in the rotated spectrum exceed a given threshold $gamma$, which is also easily computed. These are then mapped back to the original time domain. This method is enabled because of the nature of the FrFT and its ability to pull signals, especially chirp signals, out of noise. We present the signal and multipath model, and then describe how the FrFT is used to obtain the channel estimates. Through simulations, we show that this is a very accurate method, providing root mean-square error (RMSE) estimates of both channel coefficients and delays at least an order magnitude below that of existing methods, even at signal-to-noise ratios (SNRs) as low as 0dB. It is also very low in complexity, because all coefficient amplitude and delays are estimated simultaneously with few computations; it therefore offers a promising channel estimation solution for existing and future terrestrial communications systems, including 4G/5G cellular systems requiring high data rate applications.
机译:分数阶傅立叶变换(FrFT)是有用的工具,具有许多应用程序,例如通信的干扰缓解和雷达目标回波分离。在本文中,我们提出了一种新用途,即通过一个短线性调频信号通过该信道来估计未知的多径信道。多径中的多个接收到的线性调频脉冲被旋转到适当的FrFT维度,在此它们变成高功率音调,通过确定旋转频谱中的哪些值超过给定阈值$ \ gamma $可以轻松估算其幅度和延迟,这也很容易计算。然后将它们映射回原始时域。由于FrFT的性质及其将信号(尤其是线性调频信号)从噪声中拉出的能力,因此启用了此方法。我们介绍了信号和多径模型,然后描述了如何使用FrFT获得信道估计。通过仿真,我们证明这是一种非常准确的方法,即使在信噪比(SNR)的情况下,也可以提供均方根误差(RMSE)估计的信道系数,并且延迟至少比现有方法低一个数量级。 )低至0dB。它的复杂度也非常低,因为只需很少的计算就可以同时估计所有系数的幅度和延迟。因此,它为现有和未来的地面通信系统(包括需要高数据速率应用的4G / 5G蜂窝系统)提供了一种有前途的信道估计解决方案。

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