Deep-Learning Assisted Reconfigurable Intelligent Surfaces for Cooperative Communications

Sagir B., Aydin E., İlhan H.

IEEE INTERNET OF THINGS JOURNAL, vol.1, no.1, pp.1-10, 2022 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 1 Issue: 1
  • Publication Date: 2022
  • Journal Indexes: Science Citation Index Expanded, Scopus, ABI/INFORM, Compendex, INSPEC
  • Page Numbers: pp.1-10


Reconfigurable intelligent surfaces (RISs) are software-controlled passive devices to reflect incoming signals from the source ($S$) to destination ($D$), just like a relay ($R$) with optimum signal strength, improving the performance of wireless communication networks. The configurable nature of the RIS can provide network designers the flexibility to use in a stand-alone or cooperative configuration with many advantages over conventional networks. In this paper, two new deep neural networks (DNN) assisted cooperative RIS models, namely DNN$_R$\:-\:CRIS and DNN$_{R, D}$\:-\:CRIS, are proposed for cooperative communications. In these two models, the potential of RIS deployment as a relaying element in a next-generation cooperative network is investigated using deep learning (DL) techniques as a tool for optimizing the RIS. To reduce maximum likelihood (ML) complexity at the $D$, unlike the DNN$_R$\:-\:CRIS, in the DNN$_{R, D}$\:-\:CRIS model, a new DNN based symbol detection method is presented for the same network model. For a different number of relays and receiver configurations, bit error rate (BER) performance results of the proposed DNN$_R$\:-\:CRIS, DNN$_{R, D}$\:-\:CRIS models and traditional cooperative RIS (CRIS) scheme (without DNN) are presented for a multi-relay cooperative communication scenario with path loss effects.