This paper investigates the performances of multiple-input multiple-output channel and noise covariance estimation in the presence of correlated noise. The Cramer-Rao lower bounds (CRLBs) for the estimated parameters are evaluated. The optimal training sequence is designed in order to minimize the CRLB of the channel matrix estimation. When the noise covariance matrix is available, the minimum variance and unbiased estimator of the channel matrix corresponds to the generalized least squares (GLS) estimator. When the covariance matrix is unknown, we propose use of the feasible GLS technique. We prove that this two-step procedure is asymptotically equivalent to the GLS algorithm. The theoretical analysis is confirmed by Monte Carlo simulations.

中文翻译：

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MIMO系统信道和噪声协方差矩阵的可行广义最小二乘估计

本文研究了在存在相关噪声的情况下多输入多输出信道和噪声协方差估计的性能。评估估计参数的 Cramer-Rao 下限 (CRLB)。设计最优训练序列以最小化信道矩阵估计的CRLB。当噪声协方差矩阵可用时，信道矩阵的最小方差和无偏估计量对应于广义最小二乘 (GLS) 估计量。当协方差矩阵未知时，我们建议使用可行的 GLS 技术。我们证明这个两步过程渐近等价于 GLS 算法。蒙特卡罗模拟证实了理论分析。