一种带有ICI自消除的多OFDM符号时变信道估计方法 A Multiple OFDM Symbols Time-Varying Channel Estimation Method with ICI Self-Cancellation

周艳, 余小游, 孙鹏帅, 慈能达, 田丽佳, 林培英, 叶恒
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引用次数: 0

摘要

高速移动环境下,由于多普勒频移增大,信道的时变性破坏了正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)系统子载波之间的正交性,从而产生子载波间干扰(Inter Carrier Interference, ICI),导致系统性能严重下降。为了减少待估计量,通常采用基扩展模型(BEM)来近似模拟时变信道。为了提升性能,当信道的相干时间与发送码元的周期可相比拟的时候,本文采用多块(多个OFDM符号)。同时在使用导频符号对参数估计时,忽略了相邻非导频符号的ICI干扰,降低了整个时变信道下的估计准确性。因此我们通过分析子载波所产生的ICI系数变化特点,提出了一种带有ICI自消除的多OFDM符号时变信道估计方法,提高估计的准确性。但是由于基于多OFDM符号的信道估计性能在归一化多普勒频移较大时该方法成立条件不再满足,因此性能呈现明显的衰减,为了解决此问题,我们提出一种带有ICI自消除的自适应OFDM符号个数时变信道估计方法。仿真实验从误码率,归一化均方误差两方面分别验证了该方法的有效性。 In high-speed mobile environment, time-varying channel destroys the orthogonality between subcarriers of orthogonal frequency-division multiplexing (OFDM) systems, giving rise to in-ter-carrier interference (ICI), and degrading the performance of the systems. Basis expansion model (BEM) is usually used to approximate the time-varying channels to reduce the estimators. In order to improve performance, this paper adopts multi block (multiple OFDM symbols) when the coherent time of the channel variations and the symbols can be compared. However, when using pilot-symbol to parameter estimation, the ICI interference from near non-pilot-symbol is ignored, reducing its estimated accuracy of time-varying channel. And in this work the paper proposes a multiple OFDM symbols time-varying channel estimation method with ICI self-cancellation after analyzing the high correlation between subcarriers, improving the accuracy of estimation. But due to the condition of utilizing the multiple OFDM symbols channel estimation method is no longer met in large Doppler frequency shift, the performance of resulting channel estimation attenuates obviously. In order to solve this problem, we put forward a kind of adaptive OFDM symbol time-varying channel estimation method with the ICI self-cancellation. The simulation results show that the proposed method is effective from two aspects of error rate and normalized mean square error.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
一种带有ICI自消除的多OFDM符号时变信道估计方法 A Multiple OFDM Symbols Time-Varying Channel Estimation Method with ICI Self-Cancellation
高速移动环境下,由于多普勒频移增大,信道的时变性破坏了正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)系统子载波之间的正交性,从而产生子载波间干扰(Inter Carrier Interference, ICI),导致系统性能严重下降。为了减少待估计量,通常采用基扩展模型(BEM)来近似模拟时变信道。为了提升性能,当信道的相干时间与发送码元的周期可相比拟的时候,本文采用多块(多个OFDM符号)。同时在使用导频符号对参数估计时,忽略了相邻非导频符号的ICI干扰,降低了整个时变信道下的估计准确性。因此我们通过分析子载波所产生的ICI系数变化特点,提出了一种带有ICI自消除的多OFDM符号时变信道估计方法,提高估计的准确性。但是由于基于多OFDM符号的信道估计性能在归一化多普勒频移较大时该方法成立条件不再满足,因此性能呈现明显的衰减,为了解决此问题,我们提出一种带有ICI自消除的自适应OFDM符号个数时变信道估计方法。仿真实验从误码率,归一化均方误差两方面分别验证了该方法的有效性。 In high-speed mobile environment, time-varying channel destroys the orthogonality between subcarriers of orthogonal frequency-division multiplexing (OFDM) systems, giving rise to in-ter-carrier interference (ICI), and degrading the performance of the systems. Basis expansion model (BEM) is usually used to approximate the time-varying channels to reduce the estimators. In order to improve performance, this paper adopts multi block (multiple OFDM symbols) when the coherent time of the channel variations and the symbols can be compared. However, when using pilot-symbol to parameter estimation, the ICI interference from near non-pilot-symbol is ignored, reducing its estimated accuracy of time-varying channel. And in this work the paper proposes a multiple OFDM symbols time-varying channel estimation method with ICI self-cancellation after analyzing the high correlation between subcarriers, improving the accuracy of estimation. But due to the condition of utilizing the multiple OFDM symbols channel estimation method is no longer met in large Doppler frequency shift, the performance of resulting channel estimation attenuates obviously. In order to solve this problem, we put forward a kind of adaptive OFDM symbol time-varying channel estimation method with the ICI self-cancellation. The simulation results show that the proposed method is effective from two aspects of error rate and normalized mean square error.
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