Deep-Learning Based Reconfigurable Intelligent Surfaces for Intervehicular Communication

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 cascade...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2024-06, p.1-6
Main Authors: Sagir, Bulent, Aydin, Erdogan, Ilhan, Haci
Format: Article
Language:English
Subjects:
cooperative communication
deep learning (DL)
Deep neural networks (DNN)
Estimation
intervehicular communication
Nakagami distribution
Noise measurement
Optimization
reconfigurable intelligent surface (RIS)
Relays
Symbols
Vectors
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Description
Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3416879