Semi-blind (SB) channel estimation is conceived for millimeter wave (mmWave) analog-beamforming (AB) and hybrid-beamforming (HB)-based multiple-input multiple-output (MIMO) systems, which also exploits the data symbols for improving the estimation accuracy. A novel aspect of the proposed framework is that it directly estimates the analog beamformer/ combiner weights without necessitating the estimation of the entire mmWave MIMO channel matrix. By involving powerful matrix perturbation theoretic techniques, a closed-form expression is derived for the mean-squared-error (MSE) of the mmWave-AB-SB algorithm. As a further novelty, our mmWave-HB-SB technique relies on the decomposition of the channel matrix as the product of a decorrelating and a unitary matrix. Subsequently, the former is estimated purely relying on the unknown data symbols, whereas the latter is estimated exclusively from the training vectors. A lower bound on the MSE of the proposed mmWave-HB-SB technique is derived using the constrained Cramér-Rao lower bound (CRLB) framework. Furthermore, the performance gain of our mmWave-HB-SB technique over the conventional purely training-based scheme is also quantified analytically. Our simulation results demonstrate the superiority of the techniques advocated over the existing solutions and also verify the accuracy of our analytical findings.

中文翻译：

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用于毫米波 MIMO 模拟和混合波束成形中信道估计的基于二阶统计的半盲技术

半盲 (SB) 信道估计适用于基于毫米波 (mmWave) 模拟波束成形 (AB) 和混合波束成形 (HB) 的多输入多输出 (MIMO) 系统，该系统还利用数据符号来改善估计精度。所提出框架的一个新颖方面是它直接估计模拟波束成形器/组合器的权重，而无需估计整个毫米波 MIMO 信道矩阵。通过引入强大的矩阵微扰理论技术，导出了毫米波-AB-SB 算法的均方误差 (MSE) 的闭式表达式。作为进一步的创新，我们的 mmWave-HB-SB 技术依赖于信道矩阵的分解，作为去相关矩阵和酉矩阵的乘积。随后，前者纯粹依靠未知数据符号估计，而后者完全是从训练向量中估计出来的。所提出的 mmWave-HB-SB 技术的 MSE 下限是使用受约束的 Cramér-Rao 下限 (CRLB) 框架推导出来的。此外，我们的 mmWave-HB-SB 技术相对于传统的纯基于训练的方案的性能增益也进行了量化分析。我们的模拟结果证明了所提倡的技术优于现有解决方案，并验证了我们分析结果的准确性。我们的 mmWave-HB-SB 技术相对于传统的纯基于训练的方案的性能增益也进行了量化分析。我们的模拟结果证明了所提倡的技术优于现有解决方案，并验证了我们分析结果的准确性。我们的 mmWave-HB-SB 技术相对于传统的纯基于训练的方案的性能增益也进行了量化分析。我们的模拟结果证明了所提倡的技术优于现有解决方案，并验证了我们分析结果的准确性。