Chen, Z;
Tang, J;
Zhang, X;
Wu, Q;
Wang, Y;
So, DKC;
Jin, S;
(2021)
Offset Learning based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication.
IEEE Journal of Selected Topics in Signal Processing
10.1109/JSTSP.2021.3129350.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Offset Learning based Channel Estimation for Intelligent Reflecting Surface-Assisted Indoor Communication |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JSTSP.2021.3129350 |
| Publisher version: | https://doi.org/10.1109/JSTSP.2021.3129350 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Channel estimation, Training, Estimation, MIMO communication, Deep learning, 5G mobile communication, Loss measurement |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10139893 |
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