Changes between Version 13 and Version 14 of Other/Summer/2017/SpectrumClassification


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Timestamp:
Aug 11, 2017, 7:28:45 PM (7 years ago)
Author:
bbruce
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  • Other/Summer/2017/SpectrumClassification

    v13 v14  
    55== Introduction ==
    66
    7 The Smart Modem is designed to receive any signal from a USRP2, recognize the modulation scheme, and demodulate the signal. It also can be given an analog or digital signal, modulate it using a given scheme, and send it to a USRP2. To find this project, please visit [https://github.com/Avanish14/SmartModem/ the project GitHub.]
     7The Smart Modem is designed to receive a signal from a USRP2, attempt to recognize the modulation scheme, and then demodulate the signal. It also can be used to modulate and transmit signals. To find this project, please visit [https://github.com/Avanish14/SmartModem/ the project GitHub.]
    88
    99== Background ==
    1010
    11 This project utilizes machine learning algorithms to recognize the modulation schemes of incoming signals. We first generated data using GNURadio to collect representative sample vectors of signals modulated with various modulation schemes. Then, we trained a convolutional neural network with this data. The results of the training are shown below:
     11This project utilizes machine learning classifiers to recognize the modulation schemes of captured signals. We generated data using GNURadio to collect representative sample vectors of signals with various modulation schemes. Then, we trained a convolutional neural network with this data. An example confusion matrix is shown below:
    1212
    1313 {{{
    … …  
    3131}}}
    3232
    33 The neural network can always detect a signal modulated with a QAM scheme but has trouble determining the specific QAM scheme. Therefore, we use a support vector machine to accompany the neural network when it detects a signal modulated with QAM to find the specific scheme. This SVM determines [https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstat.html the 2nd and 4th k-statistic] of the QAM signal to better determine the scheme.
     33The neural network can detect a signal modulated with a QAM scheme but has trouble determining the specific QAM scheme. Therefore, we attempt to use a support vector machine to accompany the neural network when it detects a signal modulated with QAM to find the specific scheme. This SVM uses [https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstat.html the 2nd and 4th k-statistic] of the QAM signal to improve recognition. This acts as a placeholder until extraction of cyclic cumulants is achieved. (Cyclic cumulants have been shown to have very good performance at QAM recognition)
    3434 
    3535
    … …  
    3939USRP2: Software defined radio
    4040
    41 Quadro K5000: high-end GPU
     41Quadro K5000: Workstation GPU
    4242
    4343GNURadio: SDR Toolkit
    … …  
    9999}}}
    100100
     101== References ==
     102
     103https://github.com/radioML/dataset
     104
     105https://github.com/gnuradio/gnuradio
     106
     107https://github.com/tensorflow/tensorflow
     108
     109https://pdfs.semanticscholar.org/7ff6/8ad5af36a8818886e0f562f0599990fb9111.pdf
     110
     111