mmWave Channel Estimation via Compressive Covariance Estimation: Role of Sparsity and Intra-Vector Correlation

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