University of Oulu

H. Zarini, N. Gholipoor, M. R. Mili, M. Rasti, H. Tabassum and E. Hossain, "Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 5273-5278, doi: 10.1109/GLOBECOM48099.2022.10001117

Liquid state machine-empowered reflection tracking in RIS-aided THz communications

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Author: Zarini, Hosein1; Gholipoor, Narges2; Mili, Mohammad Robat3;
Organizations: 1Dept. of Computer Engineering, Sharif University of Technology, Tehran, Iran
2Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
3Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran
4Centre for Wireless Communications, University of Oulu, Finland
5Dept. of Electrical Engineering and Computer Science, York University, Canada
6Dept. of Electrical and Computer Engineering, University of Manitoba, Canada
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: IEEE, 2022
Publish Date: 2023-02-24


Passive beamforming in reconfigurable intelligent surfaces (RISs) enables a feasible and efficient way of communication when the RIS reflection coefficients are precisely adjusted. In this paper, we present a framework to track the RIS reflection coefficients with the aid of deep learning from a time-series prediction perspective in a terahertz (THz) communication system. The proposed framework achieves a two-step enhancement over the similar learning-driven counterparts. Specifically, in the first step, we train a liquid state machine (LSM) to track the historical RIS reflection coefficients at prior time steps (known as a time-series sequence) and predict their upcoming time steps. We also fine-tune the trained LSM through Xavier initialization technique to decrease the prediction variance, thus resulting in a higher prediction accuracy. In the second step, we use ensemble learning technique which leverages on the prediction power of multiple LSMs to minimize the prediction variance and improve the precision of the first step. It is numerically demonstrated that, in the first step, employing the Xavier initialization technique to fine-tune the LSM results in at most 26% lower LSM prediction variance and as much as 46% achievable spectral efficiency (SE) improvement over the existing counterparts, when an RIS of size 11×11 is deployed. In the second step, under the same computational complexity of training a single LSM, the ensemble learning with multiple LSMs degrades the prediction variance of a single LSM up to 66% and improves the system achievable SE at most 54%.

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ISBN: 978-1-6654-3540-6
ISBN Print: 978-1-6654-3541-3
Pages: 5273 - 5278
DOI: 10.1109/globecom48099.2022.10001117
Host publication: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported in part by a Discovery Grant funded by the Natural Sciences and Engineering Research Council of Canada, and in part by the University of Oulu and the Academy of Finland Profi6 336449.
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