.png){kind=icon}
.png){kind=icon}
Return
OMPL-SBL Algorithm for Intelligent Reflecting Surface-Aided mmWave Channel Estimation
.png){kind=icon}
OA
AI
DOI：10.1109/TVT.2023.3287400.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Channel estimation (CE) is critical for intelligent reflecting surface (IRS) aided millimeter wave (mmWave) multiple input multiple output (MIMO) systems. In this paper, we propose the orthogonal matching pursuit list-sparseBayesian learning (OMPL-SBL) algorithm which divides the cascaded channel estimation into two stages. The first stage calculates the prior for sparse Bayesian learning (SBL) using orthogonal matching pursuit list (OMPL) exploiting the grid sparsity of the cascaded channel, and the second stage employs the prior to obtain the accurate estimation using SBL. The proposed algorithm is able to achieve high estimation accuracy with low computational complexity compared to l(1)-minimization and Bayesian algorithms. In simulation, we show the proposed algorithm not only cuts down time complexity bymore than95% of the SBLalgorithm, but also achieves a higher estimation accuracy.
Keywords:
Channel estimation
Millimeter wave communication
Estimation
Matching pursuit algorithms
MIMO communication
Bayes methods
Sparse matrices
Sparse Bayesian learning (SBL)
intelligent reflecting surface (IRS)
channel estimation (CE)
compressed sensing (CS)
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W