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    ARTICLE

    AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy

    Zeliang An1, Tianqi Zhang1,*, Debang Liu1, Yuqing Xu2, Gert Frølund Pedersen2, Ming Shen2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2817-2834, 2023, DOI:10.32604/cmc.2023.037832

    Abstract With the advent of the Industry 5.0 era, the Internet of Things (IoT) devices face unprecedented proliferation, requiring higher communications rates and lower transmission delays. Considering its high spectrum efficiency, the promising filter bank multicarrier (FBMC) technique using offset quadrature amplitude modulation (OQAM) has been applied to Beyond 5G (B5G) industry IoT networks. However, due to the broadcasting nature of wireless channels, the FBMC-OQAM industry IoT network is inevitably vulnerable to adversary attacks from malicious IoT nodes. The FBMC-OQAM industry cognitive radio network (ICRNet) is proposed to ensure security at the physical layer to tackle the above challenge. As a… More >

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