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• W3129583548 abstract "Cross-technology interference sources act as the fundamental challenge to improve throughputs of wireless local area networks (WLAN). The quadruple generative adversarial network (QGAN) has shown its prevailing performance to identify Wi-Fi and non-Wi-Fi devices at the cost of large memory consumption and high computational complexity. To fulfill the requirement of compact neural network, a lightweight QGAN is presented in this paper with a joint compression scheme based on knowledge distillation (KD). The representation and classification sub-networks are adaptively learned through a GAN-based approach for transferring knowledge from teacher to student. Numerical results indicate that the proposed QGAN outperforms the other state-of-the-arts and achieves a satisfactory tradeoff between processing efficiency and classification accuracy." @default.
• W3129583548 created "2021-03-01" @default.
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• W3129583548 date "2020-12-11" @default.
• W3129583548 modified "2023-10-16" @default.
• W3129583548 title "Lightweight Quadruple-GAN for Interference Source Recognition in Wi-Fi Networks" @default.
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• W3129583548 doi "https://doi.org/10.1109/iccc51575.2020.9345180" @default.
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