Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems : Sparsity-Inspired Approaches

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

39 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)4078-4092
Journal / PublicationIEEE Transactions on Communications
Volume70
Issue number6
Online published20 Apr 2022
Publication statusPublished - Jun 2022
Externally publishedYes

Abstract

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter-wave (mm-wave) wireless communication. In this paper, we investigate channel estimation for IRS-assisted mm-wave multiple-input multiple-output (MIMO) wireless systems. By leveraging the sparsity of mm-wave channels in the angular domain, we formulate the channel estimation problem as an ℓ 1-norm regularized optimization problem with fixed-rank constraints. To tackle the non-convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. To further reduce the computational complexity of the estimation algorithm, we propose a compressive sensing-(CS-) based channel estimation approach. In particular, a three-stage estimation protocol is put forward where the subproblem in each stage can be solved via low-complexity CS methods. Furthermore, based on the acquired channel state information (CSI) of the cascaded channel, we design a passive beamforming algorithm for maximization of the spectral efficiency. Simulation results reveal that the proposed MO-based estimation (MO-EST) and beamforming algorithms significantly outperform two benchmark schemes while the CS-based estimation (CS-EST) algorithm strikes a balance between performance and complexity.

Research Area(s)

  • Channel estimation, Compressive sensing, Fixed-rank manifold optimization, Intelligent reflecting surface, MIMO