Deep Learning Enhanced Code Index Modulation System Empowered by Reconfigurable Intelligent Surface Technology Derin grenme Destekli Yeniden Yapilandirilabilir Akilli Y zey Teknolojisi Ile G lendirilmi s Kod Indis Mod lasyon Sistemi

Cogen F., Özden B. A., Aydın E.

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Turkey, 25 - 28 June 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu66497.2025.11111778
  • City: İstanbul
  • Country: Turkey
  • Keywords: CIM, Deep Learning, Machine Learning, RIS
  • Yıldız Technical University Affiliated: Yes

Abstract

In this study, a new system model (DNN/ML-RIS-CIM) is proposed that predicts code indices with artificial intelligence/machine learning (AI/ML) methods in a reconfigurable intelligent surface (RIS)-assisted code index modulation (CIM) system. Estimating the code indices with conventional detectors can lead to performance limitations. Therefore, instead of the code-index estimation strategy employed in the conventional CIM approach, AI- and ML-based estimators are adopted, enabling the spreading code indices selected at the transmitter side to be identified at the receiver side with higher accuracy. The proposed DNN/ML-CIM-RIS model (using a two-hidden-layer MLP for code-index estimation) captures the nonlinear patterns in despreaded chip vectors and achieves an SNR gain compared with the maximum likelihood detector (MLD).