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Deep-Learning-Assisted IoT-Based RIS for Cooperative Communications
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DOI：10.1109/JIOT.2023.3239818.png)
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摘要
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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 article, two new deep neural network (DNN)-assisted cooperative RIS (CRIS) 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 CRIS scheme (without a DNN) are presented for a multirelay 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.
Keyword:
Relays
Internet of Things
Symbols
Wireless networks
Receivers
Deep learning
Cooperative communication
Bit error rate (BER)
cooperative communication
deep learning (DL)
deep neural network (DNN)
Internet of Things (IoT)
machine learning
reconfigurable intelligent surface (RIS)
relaying
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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