论文标题
深度学习协助基于物联网的RIS进行合作通信
Deep-Learning Assisted IoT Based RIS for Cooperative Communications
论文作者
论文摘要
可重新配置的智能表面（RISS）是软件控制的被动设备，可以用作继电器（R）系统，以最佳的信号强度以提高无线通信网络的性能，以合作的方式反映从源到目的地（d）的传入信号。在基于互联网的网络（IoT）网络中部署的RI的可配置性和灵活性可以使网络设计人员能够设计出比传统网络具有相当优势的独立或合作配置。 In this paper, two new deep neural network (DNN)-assisted cooperative RIS models, namely, DNN_R-CRIS and DNN_{R, D}-CRIS, are proposed for cooperative communications.在DNN_R-CRIS模型中，使用深度学习（DL）技术研究RIS相位优化的RIS部署作为基于物联网的继电器元素的潜力。此外，为了降低D处的最大可能性（ML）复杂性，使用DNN_ {R，D} -Cris模型与DNN辅助相优化相结合，提出了一种新的基于DNN的符号检测方法。对于不同数量的继电器和接收器配置，提议的DNN_R-CRIS和DNN_ {R，D，D} -CRIS模型以及传统的合作RIS（CRIS）方案（无DNN）的位错误率（BER）性能结果是针对多R-Relay Cooperative Coomerative Communication及其路径损失效果的。据透露，拟议的基于DNN的模型在BER方面也显示出令人鼓舞的结果，即使在系统复杂性较低的高噪声环境中。
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, DNN_R-CRIS and DNN_{R, D}-CRIS, are proposed for cooperative communications. In DNN_R-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 DNN_{R,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 DNN_R-CRIS and DNN_{R, 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.
