Paper Info
Title
Offset Learning based Channel Estimation for IRS-Assisted Indoor Communication
Abstract
The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an 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
2021
10.1109/GLOBECOM46510.2021.9685156
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
DocType
ISSN
Citations 
Conference
2334-0983
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Zhen Chen151.42
Hengbin Tang241.78
Jie Tang300.68
Xiu Yin Zhang414327.26
Qingqing Wu5222890.86
Shi Jin63744274.70
Kai-Kit Wong73777281.90