Rapid CNN-Assisted Iterative RIS Element Configuration

Samed Kesir; M. Yaser Yaǧan; Ibrahim Hökelek; Ali Emre Pusane; Ali Görçin

doi:10.1109/ISNCC58260.2023.10323787

Rapid CNN-Assisted Iterative RIS Element Configuration

Samed Kesir, M. Yaser Yaǧan, Ibrahim Hökelek, Ali Emre Pusane, Ali Görçin

Department of Electronics and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.

Original language	English
Title of host publication	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350335590
DOIs	https://doi.org/10.1109/ISNCC58260.2023.10323787
Publication status	Published - 2023
Event	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 - Doha, Qatar Duration: 23 Oct 2023 → 26 Oct 2023

Publication series

Name	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

Conference

Conference	2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Country/Territory	Qatar
City	Doha
Period	23/10/23 → 26/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

ACKNOWLEDGMENT This publication was made possible in parts by NPRP13S-0130-200200 and by NPRP14C-0909-210008 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. We thank to StorAIge project that has received funding from the KDT Joint Undertaking (JU) under Grant Agreement No. 101007321. The JU receives support from the European Union’s Horizon 2020 research and innovation programme in France, Belgium, Czech Republic, Germany, Italy, Sweden, Switzerland, Türkiye, and National Authority TÜB˙TAK with project ID 121N350.

Funders	Funder number
National Authority TÜB˙TAK	121N350
Qatar National Research Fund	101007321
Horizon 2020 Framework Programme

Keywords

convolutional neural network
reconfigurable intelligent surface

Access to Document

10.1109/ISNCC58260.2023.10323787

Cite this

Kesir, S., Yaǧan, M. Y., Hökelek, I., Pusane, A. E., & Görçin, A. (2023). Rapid CNN-Assisted Iterative RIS Element Configuration. In 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 (2023 International Symposium on Networks, Computers and Communications, ISNCC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISNCC58260.2023.10323787

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abstract = "Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.",

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Kesir, S, Yaǧan, MY, Hökelek, I, Pusane, AE & Görçin, A 2023, Rapid CNN-Assisted Iterative RIS Element Configuration. in 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023. 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023, Doha, Qatar, 23/10/23. https://doi.org/10.1109/ISNCC58260.2023.10323787

Rapid CNN-Assisted Iterative RIS Element Configuration. / Kesir, Samed; Yaǧan, M. Yaser; Hökelek, Ibrahim et al.
2023 International Symposium on Networks, Computers and Communications, ISNCC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Symposium on Networks, Computers and Communications, ISNCC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Rapid CNN-Assisted Iterative RIS Element Configuration

AU - Kesir, Samed

AU - Yaǧan, M. Yaser

AU - Hökelek, Ibrahim

AU - Pusane, Ali Emre

AU - Görçin, Ali

PY - 2023

Y1 - 2023

N2 - Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.

AB - Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.

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Kesir S, Yaǧan MY, Hökelek I, Pusane AE, Görçin A. Rapid CNN-Assisted Iterative RIS Element Configuration. In 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 International Symposium on Networks, Computers and Communications, ISNCC 2023). doi: 10.1109/ISNCC58260.2023.10323787

Rapid CNN-Assisted Iterative RIS Element Configuration

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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Cite this