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

Peilan Wang, Jun Fang, Huiping Duan, Hongbin Li

Research output: Contribution to journalArticlepeer-review

431 Scopus citations

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 an accurate channel estimate and achieve a substantial training overhead reduction.

Original languageEnglish
Article number9103231
Pages (from-to)905-909
Number of pages5
JournalIEEE Signal Processing Letters
Volume27
DOIs
StatePublished - 2020

Keywords

  • Intelligent reflecting surface
  • channel estimation
  • millimeter wave communications

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