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mmWave Channel Estimation via Compressive Covariance Estimation: Role of Sparsity and Intra-Vector Correlation
IEEE Transactions on Signal Processing ( IF 5.4 ) Pub Date : 2021-04-13 , DOI: 10.1109/tsp.2021.3070210
Dheeraj Prasanna , Chandra R. Murthy

In this work, we address the problem of multiple-input multiple-output mmWave channel estimation in a hybrid analog-digital architecture, by exploiting both the underlying spatial sparsity as well as the spatial correlation in the channel. We accomplish this via compressive covariance estimation, where we estimate the channel covariance matrix from noisy low dimensional projections of the channel obtained in the pilot transmission phase. We use the estimated covariance matrix as a plug-in to the linear minimum mean square estimator to obtain the channel estimate. We present a new Gaussian prior model, inspired by sparse Bayesian learning (SBL), which incorporates parameters to capture the channel correlation in addition to sparsity. Based on this prior, we develop the Corr-SBL algorithm, which uses an expectation maximization procedure to learn the parameters of the prior and update the posterior channel estimates. A closed form solution is obtained for the maximization step based on fixed-point iterations. To facilitate practical implementation, an online version of the algorithm is developed which significantly reduces the latency at a marginal loss in performance. The efficacy of the prior model is studied by analyzing the normalized mean squared error in the channel estimate. Our results show that, when compared to a genie-aided estimator and other existing sparse recovery algorithms, exploiting both sparsity and correlation results in significant performance gains, even under imperfect covariance estimates obtained using a limited number of samples.

中文翻译:

通过压缩协方差估计的毫米波信道估计:稀疏性和矢量内相关的作用

在这项工作中,我们通过利用基础空间稀疏性以及信道中的空间相关性,解决了混合模数架构中多输入多输出mmWave信道估计的问题。我们通过压缩协方差估计来完成此任务,在压缩协方差估计中,我们根据在导频传输阶段获得的噪声小的低维投影来估计信道协方差矩阵。我们使用估计的协方差矩阵作为线性最小均方估计器的插件来获得信道估计。在稀疏贝叶斯学习(SBL)的启发下,我们提出了一种新的高斯先验模型,该模型除稀疏性外还包含参数以捕获通道相关性。基于此先验,我们开发了Corr-SBL该算法使用期望最大化过程来学习先验的参数并更新后验信道估计。在定点迭代的基础上,针对最大化步骤获得了封闭形式的解决方案。为了便于实际实施,开发了该算法的在线版本,该版本显着减少了性能略有下降的等待时间。通过分析信道估计中的归一化均方误差来研究先验模型的有效性。我们的结果表明,与使用遗传算法的估计器和其他现有的稀疏恢复算法相比,即使在使用有限数量的样本获得的协方差估计不完美的情况下,利用稀疏性和相关性也会显着提高性能。
更新日期:2021-04-30
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