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Frequency-Offset Estimation for MIMO and OFDM Systems Using Orthogonal Training Sequences

机译:使用正交训练序列的MIMO和OFDM系统的频偏估计

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摘要

We propose a training-sequence-based frequency-offset estimator for a multiple transmit-and-receive antenna system in frequency-flat fading channels. The estimator is based on the maximum likelihood (ML) criterion and does not require channel information. To reduce the computational load, they propose to use special training sequences-the periodic orthogonal codes. Using these codes, we get a closed form estimator which requires much lower computational load (some additions and multiplications). For the high signal-to-noise ratio and small frequency offset, the proposed estimator achieves the performance of the optimal ML estimator, which locates the peak of the likelihood function. We also apply the proposed estimator to a multiple antenna system in frequency-selective channels and an orthogonal-frequency-division-multiplexing system. With theoretical analysis and simulations, we evaluate the performance of the proposed estimator
机译:我们为频率平坦衰落信道中的多个发射和接收天线系统提出了一种基于训练序列的频偏估计器。估计器基于最大似然(ML)标准,并且不需要信道信息。为了减少计算量,他们建议使用特殊的训练序列-周期性正交码。使用这些代码,我们得到了一个封闭形式的估计器,它需要低得多的计算量(一些加法和乘法)。对于高信噪比和较小的频率偏移,提出的估计器实现了最佳ML估计器的性能,该算法确定了似然函数的峰值。我们还将拟议的估计器应用于频率选择信道中的多天线系统和正交频分复用系统。通过理论分析和仿真,我们评估了拟议估算器的性能

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