Abstract
The acquisition of channel state information is crucial in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, the previous studies for mmWave channel estimation only focus on the conventional static channel model without considering the Doppler shifts in a time-varying scenario. Since the variations of angles are much shorter than that of path gains, the mmWave time-varying channel has block-sparse and low-rank characteristics. In this paper, we show that the block sparsity, along with the low-rank structure, can be utilized to extract the Doppler shifts and other channel parameters. Specially, to effectively exploit the block-sparse and low-rank structures, a two-stage method is proposed for mmWave time-varying channel estimation. In the first stage, we formulate a block-sparse signal recovery problem for AoAs/AoDs estimation, and we develop a block orthogonal matching pursuit (BOMP) algorithm to estimate the AoAs/AoDs. In the second stage, we formulate a low-rank tensor due to the low-rank structure of time-varying channels, and based on the results of the first stage, a CANDECOMP/PARAFAC (CP) decomposition-based algorithm is proposed to estimate the Doppler shifts and path gains. In addition, in order to compare with conventional tensor decomposition-based algorithms, two tensor decomposition-based time-varying channel estimation algorithms are proposed. Simulation results demonstrate that the proposed channel estimation algorithm outperforms the conventional compressed sensing-based algorithms and the tensor decomposition-based algorithms, and the proposed algorithm remains close to the Cramer-Rao Lower Bound (CRLB) even in the low SNR region with the priori knowledge of AoAs/AoDs.
Highlights
Global mobile system capacity will increase dramatically in the coming years, driven by ultra high definition videos, smart vehicular communications, and virtual reality etc
A novel channel estimation algorithm by exploiting the block-sparse and low-rank characteristics is proposed for time-varying mmWave massive multiple-input multiple-output (MIMO) systems
Since the locations of non-zero blocks are not related to the Doppler shifts, the above angle estimation algorithms are robust to different Doppler shifts
Summary
Global mobile system capacity will increase dramatically in the coming years, driven by ultra high definition videos, smart vehicular communications, and virtual reality etc. L. Cheng et al.: mmWave Time-Varying Channel Estimation via Exploiting Block-Sparse and Low-Rank Structures. The study in [18] has proposed an adaptive estimation (AAE) algorithm for the time-varying mmWave channel It suffers from the overhead transmission from the receiver to the transmitter. A novel channel estimation algorithm by exploiting the block-sparse and low-rank characteristics is proposed for time-varying mmWave massive MIMO systems. We formulate a block-sparse signal recovery problem for AoAs/AoDs estimation, and we develop a block orthogonal matching pursuit (BOMP) algorithm to estimate the AoAs/AoDs. In the second stage, we formulate a low-rank tensor due to the low-rank structure of time-varying channels, and based on the results of the first stage, we propose a CANDECOMP/PARAFAC (CP) decomposition-based algorithm to estimate the Doppler shifts and path gains. A B is the Khatri-Rao product of matrices A and B
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