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Spatial Reciprocity Aided CSI Acquirement for HST Massive MIMO

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Human Centered Computing (HCC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12634))

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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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Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China

    Kaihang Zheng, Yinglei Teng & Mei Song

  2. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    An Liu

Authors
  1. Kaihang Zheng
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  2. Yinglei Teng
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  3. An Liu
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  4. Mei Song
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Corresponding author

Correspondence to Kaihang Zheng .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, Hubei, China

    Qiaohong Zu

  2. South China Normal University, Guangzhou, China

    Yong Tang

  3. University of Kragujevac, Kragujevac, Serbia

    Vladimir Mladenović

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70625-8

  • Online ISBN: 978-3-030-70626-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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