Deep-Learning Assisted Reconfigurable Intelligent Surfaces for Cooperative Communications


Sagir B., Aydin E., İlhan H.

IEEE INTERNET OF THINGS JOURNAL, cilt.1, sa.1, ss.1-10, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1109/jiot.2023.3239818
  • Dergi Adı: IEEE INTERNET OF THINGS JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Compendex, INSPEC
  • Sayfa Sayıları: ss.1-10
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.