Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach

Xi Zheng, Peilan Wang, Jun Fang, Hongbin Li

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our proposed method is as low as O(U2) , where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method.

Original languageEnglish
Pages (from-to)1258-1262
Number of pages5
JournalIEEE Wireless Communications Letters
Volume11
Issue number6
DOIs
StatePublished - 1 Jun 2022

Keywords

  • Intelligent reflecting surface
  • channel estimation
  • millimeter wave communications

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