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Learning Time-Frequency Analysis in Wireless Sensor Networks
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DOI：10.1109/JIOT.2017.2771514.png)
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摘要
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Time-frequency analysis is one of essential signal processing tool for wireless sensor signal in Internet of Things (IoT), but its traditional processing approaches, such as short time-frequency transformation (STFT) and discrete wavelet transformation (DWT), are challenged by the limitation of capability to self-learn from unknown environments and adjust parameters adaptively. To address this problem, we propose to build up the deep learning network to learn time-frequency analysis to instead of traditional STFT and DWT approaches. With using typical neural network layers to remodel STFT and DWT operations, the proposed models consider the efficiency on both training and processing procedures and show their parameter adaptability and capability of deep feature extraction from sensor signals. Moreover, we demonstrate how to integrate learning time-frequency analysis networks into practical IoT applications, signal detection in noisy environment, and classifying of various modulated wireless sensor signal, by which their performance are further evaluated in terms of computation complexity and efficiency.
Keyword:
Deep learning
time-frequency analysis
wireless sensor network (WSN)
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8.9
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1.4W
被引数:
7.8W
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