Deep-Learning Based Reconfigurable Intelligent Surfaces for Intervehicular Communication

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Sagir B., AYDIN E., İLHAN H.

IEEE Transactions on Vehicular Technology, cilt.73, sa.11, ss.17754-17759, 2024 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 73 Sayı: 11
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/tvt.2024.3416879
  • Dergi Adı: IEEE Transactions on Vehicular Technology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.17754-17759
  • Anahtar Kelimeler: cooperative communication, deep learning (DL), Deep neural networks (DNN), intervehicular communication, reconfigurable intelligent surface (RIS)
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

This paper proposes a novel deep neural network (DNN) assisted cooperative reconfigurable intelligent surface (RIS) scheme and a DNN-based symbol detection model for intervehicular communication. In the considered realistic channel model, the channel links between moving nodes are modeled as cascaded Nakagami-$m$ channels, and the links involving any stationary node are modeled as Nakagami-$m$ fading channels, where all nodes between source and destination are realized with RIS-based relays. The performances of the proposed models are evaluated and compared against the conventional methods in terms of bit error rate (BER) and computational complexity. It is shown that the proposed DNN-based systems achieve almost the same performance as conventional systems with low system complexity.