Paper Info
Title
Offset Learning Based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication
Abstract
The emerging intelligent reflecting surface (IRS) can significantly improve the system capacity, and it has been regarded as a promising technology for the beyond fifth-generation (B5G) communications. For IRS-assisted multiple input multiple output (MIMO) systems, accurate channel estimation is a critical challenge. This severely restricts practical applications, particularly for resource-limited indoor scenario as it contains numerous scatterers and parameters to be estimated, while the number of pilots is limited. Prior art tackles these issues and associated optimization using mathematical-based statistical approaches, but are difficult to solve as the number of scatterers increase. To estimate the indoor channels with an affordable piloting overhead, we propose an offset learning (OL)-based neural network for channel estimation. The proposed OL-based estimator can dynamically trace the channel state information (CSI) without any prior knowledge of the IRS-assisted channel structure as well as indoor statistics. In addition, inspired by the powerful learning capability of convolutional neural network (CNN), CNN-based inversion blocks are developed in the offset estimation module to build the offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.
Year
DOI
Venue
2022
10.1109/JSTSP.2021.3129350
IEEE Journal of Selected Topics in Signal Processing
Keywords
DocType
Volume
Indoor 5G,indoor channel estimation,massive MIMO,deep learning,intelligent reflecting surface (IRS)
Journal
16
Issue
ISSN
Citations 
1
1932-4553
1
PageRank 
References 
Authors
0.36
0
8
Name
Order
Citations
PageRank
Zhen Chen131.41
Jie Tang28910.90
Xiu Yin Zhang314327.26
Qingqing Wu4222890.86
Yuxin Wang510.70
Daniel K. C. So610.36
Shi Jin7100.82
Kai-Kit Wong83777281.90