This letter proposes a novel deep neural network (DNN) assisted cooperative reconfigurable intelligent surface (RIS) scheme and a DNN-based symbol detection model for intervehicular communication over cascaded Nakagami-m fading channels. 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 with the conventional methods in terms of bit error rates (BER). It is exhibited that the DNN-based systems show near-identical performance with low system complexity.
@article{arxiv.2305.12546,
title = {Deep-Learning Based Reconfigurable Intelligent Surfaces for Intervehicular Communication},
author = {Bulent Sagir and Erdogan Aydin and Haci Ilhan},
journal= {arXiv preprint arXiv:2305.12546},
year = {2023}
}