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• W2799873674 startingPage "754" @default.
• W2799873674 abstract "Aiming at a massive multi-input multi-output (MIMO) system with unknown channel path number, a sparse adaptive compressed sensing channel estimation algorithm is proposed, which is the block sparsity adaptive matching pursuit (BSAMP) algorithm. Based on the joint sparsity of subchannels in massive MIMO systems, the initial set of support elements can be quickly and selectively selected by setting the threshold and finding the maximum backward difference position. At the same time, the energy dispersal caused by the non-orthogonality of the observation matrix is considered, and the estimation performance of the algorithm is improved. The regularization of the elements secondary screening is deployed, in order to improve the stability of the algorithm. Simulation results show that the proposed algorithm can quickly and accurately recover massive MIMO channel state information with unknown channel sparsity and high computational efficiency compared with other algorithms." @default.
• W2799873674 created "2018-05-17" @default.
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• W2799873674 date "2018-05-09" @default.
• W2799873674 modified "2023-10-18" @default.
• W2799873674 title "Compressive Sensing-based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems" @default.
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• W2799873674 doi "https://doi.org/10.3390/app8050754" @default.
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