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In this paper, channel estimation in millimeter wave (mmWave) communication systems is considered. In contrast to prevailing mmWave channel estimation methods exploiting the sparsity nature of the channel, we move one step further by exploiting the joint AoD-AoA angular spread. By formulating the channel estimation as a block-sparse signal recovery with an underlying two-dimensional cluster feature, we propose a two-dimensional sparse Bayesian learning method without a priori knowledge of two-dimensional angular spread patterns. It essentially couples the channel path power at one angular direction with its two-dimensional AoD-AoA neighboring directions. Compared with existing sparse mmWave channel estimation methods, the proposed method is numerically verified to reduce the training overhead and channel estimation error.
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