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Joint frequency offset, time offset, and channel estimation for OFDM/OQAM systems

机译:OFDM / OQAM系统的联合频率偏移,时间偏移和信道估计

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Among the multicarrier modulation techniques considered as an alternative to orthogonal frequency division multiplexing (OFDM) for future wireless networks, a derivative of OFDM based on offset quadrature amplitude modulation (OFDM/OQAM) has received considerable attention. In this paper, we propose an improved joint estimation method for carrier frequency offset, sampling time offset, and channel impulse response, needed for the practical application of OFDM/OQAM. The proposed joint ML estimator instruments a pilot-based maximum-likelihood (ML) estimation of the unknown parameters, as derived under the assumptions of Gaussian noise and independent input symbols. The ML estimator formulation relies on the splitting of each received pilot symbol into contributions from surrounding pilot symbols, non-pilot symbols and additive noise. Within the ML framework, the Cramer-Rao bound on the covariance matrix of unbiased estimators of the joint parameter vector under consideration is derived as a performance benchmark. The proposed method is compared with a highly cited previous work. The improvements in the results point to the superiority of the proposed method, which also performs close to the Cramer-Rao bound.
机译:在被认为是未来无线网络的正交频分复用(OFDM)的替代方法的多载波调制技术中,基于偏移正交幅度调制(OFDM / OQAM)的OFDM派生技术已受到相当大的关注。本文针对OFDM / OQAM的实际应用,提出了一种针对载波频率偏移,采样时间偏移和信道冲激响应的改进联合估计方法。所提出的联合ML估计器可对未知参数进行基于飞行员的最大似然(ML)估计,该估计是在高斯噪声和独立输入符号的假设下得出的。 ML估计器公式依赖于将每个接收到的导频符号分解为周围导频符号,非导频符号和附加噪声的贡献。在ML框架内,考虑中的联合参数向量的无偏估计量的协方差矩阵的Cramer-Rao界被推导出为性能基准。所提出的方法与以前被高度引用的工作进行了比较。结果的改进指出了所提出方法的优越性,该方法的性能也接近于Cramer-Rao界。

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