Deep-Learning-Assisted IoT-Based RIS for Cooperative Communications

Abstract

Reconfigurable intelligent surfaces (RISs) are software-controlled passive devices that can be used as relay (R) systems to reflect incoming signals from a source (S) to a destination (D) in a cooperative manner with optimum signal strength to improve the performance of wireless communication networks. The configurability and flexibility of an RIS deployed in an Internet-of-Things (IoT)-based network can enable network designers to devise stand-alone or cooperative configurations that have considerable advantages over conventional networks. In this paper, two new deep neural network (DNN)-assisted cooperative RIS models, namely, DNNR-CRIS and DNNR,D-CRIS, are proposed for cooperative communications. In DNNR-CRIS model, the potential of RIS deployment as an IoT-based relay element in a next-generation cooperative network is investigated using deep learning (DL) techniques for RIS phase optimization. In addition, to reduce the maximum likelihood (ML) complexity at D, a new DNN-based symbol detection method is presented with the DNNR,D-CRIS model combined with DNN-assisted phase optimization. For a different number of relays and receiver configurations, the bit error rate (BER) performance results of the proposed DNNR-CRIS and DNNR,D-CRIS models and traditional cooperative RIS (CRIS) scheme (without a DNN) are presented for a multi-relay cooperative communication scenario with path loss effects. It is revealed that the proposed DNN-based models show promising results in terms of BER, even in high-noise environments with low system complexity.