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Improvement and comparison of Modulus Algorithm Blind Equalizer learning curve in time and frequency domain

机译:模量算法盲均衡器在时间和频域中学习曲线的改进与比较

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In this paper, we intent to realize the unknown channel and equalize the distorted signal simultaneously. Therefore, channel estimation is required in order to equalize the signal in the receiver side. However, the problem is that there is no information about the reference signal. Therefore, Blind Equalizer is suggested. Blind equalizers do not require any known training sequence for the startup period, but can rather perform at any time the equalization directly on the data stream. Moreover, due to utilization of Blind Equalizer, the pilot signal can be cut off so that the amount of transmitted data is reduced. Thus faster computational process is expected compare to conventional equalizer. In this paper, conventional method of equalization, Constant Modulus Equalizer, Modified Constant Modulus Equalizer and frequency domain process of Constant Modulus Equalizer and Modified Constant Modulus Equalizer are introduced, respectively. In order to evaluate the each scheme, we have analyzed with a simple multipath channel and calculated the Normalized Mean Square Error (NMSE). Also the computational complexity is evaluated for suggested schemes. Consequently, the Modified Constant Modulus Equalizer in time domain performed the lowest NMSE among given method. However, computational complexity point view of this paper, Frequency Domain Modified Constant Modulus Equalizer has the lowest complexity.
机译:在本文中,我们意图实现了未知信道并同时均衡失真信号。因此,需要信道估计以使接收器侧的信号均衡。但是,问题是没有关于参考信号的信息。因此,建议盲均衡器。盲均衡器不需要用于启动时段的任何已知的训练序列,而是可以在任何时候直接在数据流上进行均衡。此外,由于盲均衡器的利用,可以切断导频信号,使得透射数据的量减小。因此,预期更快的计算过程与传统均衡器相比。在本文中,介绍了常规均衡,恒定模量均衡器,改性恒定模量均衡器和恒定模量均衡器和修改恒定模量均衡器的频域处理的传统方法。为了评估每个方案,我们已经用简单的多径通道分析并计算了归一化均方误差(NMSE)。还针对建议的方案评估计算复杂性。因此,在时域中的修改的常量模量均衡器在给定方法中执行了最低的NMSE。然而,本文的计算复杂性点视图,频域修正的恒定模量均衡器具有最低的复杂性。

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