Rapid CNN-Assisted Iterative RIS Element Configuration

Creative Commons License

Kesir S., Yaǧan M. Y., Hökelek I., Pusane A. E., Görçin A.

2023 International Symposium on Networks, Computers and Communications, ISNCC 2023, Doha, Qatar, 23 - 26 October 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/isncc58260.2023.10323787
  • City: Doha
  • Country: Qatar
  • Keywords: convolutional neural network, reconfigurable intelligent surface
  • Istanbul Technical University Affiliated: Yes


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.