articleIEEE Signal Processing LettersJan 1, 2020GREEN OA

Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems

PWPeilan WangJFJun FangHDHuiping DuanHLHongbin Li

University of Electronic Science and Technology of China · Stevens Institute of Technology

Indexed inarxivcrossref

Abstract

In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide…

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499
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32.46
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100%
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Authors

4
  • PW
    Peilan WangCorresponding

    University of Electronic Science and Technology of China

  • JF
    Jun Fang

    University of Electronic Science and Technology of China

  • HD
    Huiping Duan

    University of Electronic Science and Technology of China

  • HL
    Hongbin Li

    Stevens Institute of Technology

Topics & keywords

Topics
Keywords
  • Channel (broadcasting)
  • Compressed sensing
  • Cascade
  • Overhead (engineering)
  • Transmission (telecommunications)
  • Sparse matrix
  • Kronecker delta
  • Extremely high frequency
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