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Online modulation recognition of analog communication signals using neural network

机译:使用神经网络在线模拟通信信号的调制识别

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In this paper, a neural network based online analog modulation recognition of communication signals is presented. The proposed system can discriminate between amplitude modulation (AM), frequency modulation (FM), double sideband (DSB), upper sideband (USB), lower sideband (LSB) and continuous wave (CW) modulations. A matlab graphical user interface (GUI) is designed to see the intercepted signal, its power spectral density, frequency and modulation type on the screen of the personnel computer. To achieve correct classification, extensive simulations have been done for training the neural network. Theoretical simulations and experimental results indicate good performance even at signal-to-noise ratios as low as 5 dB.
机译:本文提出了一种基于神经网络的通信信号在线模拟调制识别方法。拟议的系统可以区分幅度调制(AM),频率调制(FM),双边带(DSB),上边带(USB),下边带(LSB)和连续波(CW)调制。 Matlab图形用户界面(GUI)旨在在人机屏幕上查看截获的信号,其功率谱密度,频率和调制类型。为了实现正确的分类,已经进行了广泛的模拟来训练神经网络。理论仿真和实验结果表明,即使在信噪比低至5 dB的情况下也具有良好的性能。

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