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Channel Estimation for mmWave Massive MIMO via Phase Retrieval

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Advanced Hybrid Information Processing (ADHIP 2018)

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Abstract

The research on channel estimation technology is a core technology for mmWave massive MIMO in 5G wireless communications. This paper proposed a greedy iterative phase retrieval algorithm for channel estimation from received signal strength (RSS) feedback which is common in wireless communication systems and is used to compensate for temporal channels. We consider a Modified Gauss-Newton (MGN) algorithm to approximate the square term of the system model as a linear problem at each iteration and it is embedded in the 2-opt framework for iteration to get the optimal estimation. Our algorithm does not need to modify the system, but only need RSS feedback for channel estimation. The simulation results show that the algorithm performs better than the traditional conventional algorithm.

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Acknowledgment

This work was supported by the Natural Science Foundation of Fuyang Normal University (2015KJ007) and the Horizontal project of Fuyang Normal University (XDHX201741).

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Authors and Affiliations

  1. College of Electronic and Optical Engineering and College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, China

    Zhuolei Xiao & Yunyi Li

  2. School of Computer and Information Engineering, Fuyang Normal University, Fuyang, China

    Zhuolei Xiao

  3. College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

    Guan Gui

Authors
  1. Zhuolei Xiao
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  2. Yunyi Li
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  3. Guan Gui
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Corresponding author

Correspondence to Zhuolei Xiao .

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Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Shuai Liu

  2. Hunan City University, Yiyang, China

    Gelan Yang

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

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Xiao, Z., Li, Y., Gui, G. (2019). Channel Estimation for mmWave Massive MIMO via Phase Retrieval. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_39

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  • DOI: https://doi.org/10.1007/978-3-030-19086-6_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19085-9

  • Online ISBN: 978-3-030-19086-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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