Deep-Learning Based Reconfigurable Intelligent Surfaces for Intervehicular Communication


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Sagir B., AYDIN E., İLHAN H.

IEEE Transactions on Vehicular Technology, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1109/tvt.2024.3416879
  • Journal Name: IEEE Transactions on Vehicular Technology
  • Journal Indexes: 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
  • Keywords: cooperative communication, deep learning (DL), Deep neural networks (DNN), Estimation, intervehicular communication, Nakagami distribution, Noise measurement, Optimization, reconfigurable intelligent surface (RIS), Relays, Symbols, Vectors
  • Yıldız Technical University Affiliated: Yes

Abstract

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.