Baghaki Ali, Champagne Benoit
Department of Electrical and Computer Engineering, McGill University, 3480 University Street, Montreal, H3A 0E9 Canada.
EURASIP J Adv Signal Process. 2018;2018(1):4. doi: 10.1186/s13634-017-0526-4. Epub 2018 Jan 8.
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.
在被视为未来无线网络中正交频分复用（OFDM）替代方案的多载波调制技术中，基于偏移正交幅度调制（OFDM/OQAM）的OFDM衍生物受到了广泛关注。在本文中，我们针对OFDM/OQAM实际应用所需的载波频率偏移、采样时间偏移和信道冲激响应，提出了一种改进的联合估计方法。所提出的联合最大似然（ML）估计器采用基于导频的最大似然估计来估计未知参数，该估计是在高斯噪声和独立输入符号的假设下推导得出的。ML估计器公式依赖于将每个接收到的导频符号分解为来自周围导频符号、非导频符号和加性噪声的贡献。在ML框架内，推导了所考虑的联合参数向量无偏估计器协方差矩阵的克拉美 - 罗界作为性能基准。将所提出的方法与之前一篇被大量引用的工作进行了比较。结果的改进表明了所提出方法的优越性，该方法的性能也接近克拉美 - 罗界。
