Rapid CNN-Assisted Iterative RIS Element Configuration

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publication2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350335590
Publication statusPublished - 2023
Event2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 - Doha, Qatar
Duration: 23 Oct 202326 Oct 2023

Publication series

Name2023 International Symposium on Networks, Computers and Communications, ISNCC 2023


Conference2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


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.

FundersFunder number
National Authority TÜB˙TAK121N350
Qatar National Research Fund101007321
Horizon 2020 Framework Programme


    • convolutional neural network
    • reconfigurable intelligent surface


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