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A Low-Complexity Channel Estimation Method Based on Subspace for Large-Scale MIMO Systems

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Wireless and Satellite Systems (WiSATS 2019)

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Abstract

In large-scale multiple-input multiple-output (LS-MIMO) systems, singular value decomposition (SVD) or eigenvalue decomposition (EVD) are common channel estimation schemes. However, the computational complexity of two estimators limits the application in LS-MIMO systems. Motivated by this, in order to reduce the complexity, a novel method that combines fast single compensation approximated power iteration (FSCAPI) algorithm with iterative least square with projection (ILSP), FSCAPI-ILSP, is proposed in this paper, In the proposed method, the received signals subspace is estimated by the FSCAPI algorithm firstly, then the initial channel estimation is obtained by the pilot signals. Finally, we combine it with the ILSP algorithm to improve the accuracy of the channel estimation. Compared with the conventional methods, the proposed scheme degrades the computational complexity significantly. Simulated results indicate the provided method is better than its counterparts and improves the channel estimation accuracy effectively.

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Acknowledgement

This work is supported in part by the National Natural Science Foundation of China (No. 61571108 & No. 61701197), the open research fund of National Mobile Communications Research Laboratory of millimeter wave, Southeast University (No. 2018D15), the open research fund of the National Key Laboratory of millimeter wave, Southeast University (No. K201918), and the Open Foundation of Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunication (No. 2017WICOM01), the Programme of Introducing Talents of Discipline to Universities (the 111 project, No. B12018), Postgraduate Research & Practice Innovation Program of Jiangsu Provence (No. SJCX18_0646), China Postdoctoral Science Foundation (No. 2018M641354).

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Authors and Affiliations

  1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, 214122, China

    Cheng Zhou, Zhengquan Li, Qiong Wu, Yang Liu, Baolong Li & Xiaoqing Zhao

  2. National Mobile Communication Research Laboratory, Southeast University, Nanjing, 210096, China

    Zhengquan Li & Qiong Wu

  3. Department of Information Systems, California State University, Los Angeles, USA

    Song Xing

  4. The National Key Laboratory of Millimeter Wave, Southeast University, Nanjing, 210096, China

    Yang Liu

  5. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Qiong Wu

Authors
  1. Cheng Zhou
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  2. Zhengquan Li
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  3. Song Xing
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  4. Qiong Wu
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  5. Yang Liu
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  6. Baolong Li
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  7. Xiaoqing Zhao
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Corresponding author

Correspondence to Zhengquan Li .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Min Jia

  2. Harbin Institute of Technology, Harbin, China

    Qing Guo

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhou, C. et al. (2019). A Low-Complexity Channel Estimation Method Based on Subspace for Large-Scale MIMO Systems. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_36

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  • DOI: https://doi.org/10.1007/978-3-030-19156-6_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19155-9

  • Online ISBN: 978-3-030-19156-6

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