Abstract
In this letter, we formulate an off-grid channel model to characterize spatial sample mismatching in the discrete Fourier transform (DFT) based massive multiple-input-multiple-output (MIMO) channel estimation over the millimeter-wave (mmWave) band. Then, we decompose the off-grid mmWave massive MIMO channel estimation into the learning of model parameters and virtual channel estimation. Specifically, an expectation maximization (EM) based sparse Bayesian learning framework is first developed to learn the model parameters, such as bias parameters and spatial signatures, with unknown noise. With the learned model parameters, we resort to the linear minimum mean square error method to estimate the instantaneous virtual channel with less pilot overhead. Finally, we corroborate the validity of the proposed method through numerical simulations.
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