Deep-Learning Based Reconfigurable Intelligent Surfaces for Intervehicular Communication

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.

Deep-Learning Assisted Reconfigurable Intelligent Surfaces for Cooperative Communications

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.