Abstract
This paper proposes a channel estimation scheme for large-scale multiple-input multiple-output (MIMO) systems in high-speed train (HST) scenarios. On the premise that the priori velocity is accurate, we introduce fuzzy prior spatial knowledge and design a sparse received signal model with dynamic grids. After reconstructing the channel estimation into a sparse Bayesian learning (SBL) parameter estimation problem, the maximization-minimization (MM) algorithm is adopted to solve the problem, and a fast searching algorithm based on significant gradient is proposed to solve the multi-peak optimization problem of the surrogate function. Finally, the simulation verifies that the scheme can converge quickly and has accurate estimation results.
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Acknowledgement
This work was supported in part by the National Key R&D Program of China (No. 2018YFB1201500), the National Natural Science Foundation of China under Grant No. 61771072.
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Zheng, K., Teng, Y., Liu, A., Song, M. (2021). Spatial Reciprocity Aided CSI Acquirement for HST Massive MIMO. In: Zu, Q., Tang, Y., Mladenović, V. (eds) Human Centered Computing. HCC 2020. Lecture Notes in Computer Science(), vol 12634. Springer, Cham. https://doi.org/10.1007/978-3-030-70626-5_4
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DOI: https://doi.org/10.1007/978-3-030-70626-5_4
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