.png){kind=icon}
.png){kind=icon}
Return
Exploiting Structured Sparsity With Low Complexity Sparse Bayesian Learning for RIS-Assisted MIMO mmWave Channel Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2023.3342864.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As an emerging communication auxiliary technology, reconfigurable intelligent surface (RIS) is expected to play a significant role in the upcoming 6G networks. Due to its total reflection characteristics, it is challenging to implement conventional channel estimation algorithms. This work focuses on RIS-assisted MIMO communications. Although many algorithms have been proposed to address this issue, there are still ample opportunities for improvement in terms of estimation accuracy, complexity, and applicability. To fully exploit the structured sparsity of the multiple-input-multiple-output (MIMO) channels, we propose a new channel estimation algorithm called unitary approximate message passing sparse Bayesian learning with partial common support identification (UAMPSBL-PCI). Thanks to the mechanism of PCI and the use of UAMP, the proposed algorithm has a lower complexity while delivering enhanced performance relative to existing channel estimation algorithms. Extensive simulations demonstrate its excellent performance in various environments.
Keywords:
Channel estimation
Millimeter wave communication
MIMO communication
Estimation
Complexity theory
Approximation algorithms
Reflection
Reconfigurable intelligent surface
channel estimation
unitary approximate message passing
sparse Bayesian learning
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W