A Numerical Study of Cosmic Proton Modulation Using AMS-02 Observations

A novel and efficient end-to-end learning model for automatic modulation classification (AMC) is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature (IQ) data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks (SCRNNs) are developed to take complementary advantage of parallel computing capability of convolutional neural networks (CNNs) and temporal sensitivity of recurrent neural networks (RNNs). Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio (SNR) range above -10 dB, and achieves significantly improved classification accuracy from 80% to 92.1% at high SNRs, while drastically reduces the training and prediction time by approximately 74% and 67%, respectively. Furthermore, a comparative study is performed to investigate the impacts of various SCRNN structure settings on classification performance. A representative SCRNN architecture with the two-layer CNN and subsequent two-layer long short-term memory (LSTM) is developed to suggest the option for fast AMC.

[1]  Yonina C. Eldar,et al.  Fast Deep Learning for Automatic Modulation Classification , 2019, ArXiv.

[2]  Shilian Zheng,et al.  Fusion Methods for CNN-Based Automatic Modulation Classification , 2019, IEEE Access.

[3]  T. Charles Clancy,et al.  Over-the-Air Deep Learning Based Radio Signal Classification , 2017, IEEE Journal of Selected Topics in Signal Processing.

[4]  Jerry M. Mendel,et al.  Maximum-likelihood classification for digital amplitude-phase modulations , 2000, IEEE Trans. Commun..

[5]  Zhiping Lin,et al.  Automatic Modulation Classification of Cochannel Signals using Deep Learning , 2018, 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP).

[6]  Zhifeng Yun,et al.  Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels , 2008, IEEE Transactions on Wireless Communications.

[7]  Timothy J. O'Shea,et al.  Radio Machine Learning Dataset Generation with GNU Radio , 2016 .

[8]  MengChu Zhou,et al.  Likelihood-Ratio Approaches to Automatic Modulation Classification , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Brian M. Sadler,et al.  Hierarchical digital modulation classification using cumulants , 2000, IEEE Trans. Commun..

[10]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..

[11]  Christian Weber,et al.  Automatic modulation classification technique for radio monitoring , 2015 .

[12]  Samir S. Soliman,et al.  Automatic modulation classification using zeroç crossing , 1990 .

[13]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[14]  Zilong Zhang,et al.  Automatic modulation classification using recurrent neural networks , 2017, 2017 3rd IEEE International Conference on Computer and Communications (ICCC).

[15]  Ya Tu,et al.  Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks , 2018, IEEE Access.

[16]  Erik G. Larsson,et al.  Adversarial Attacks on Deep-Learning Based Radio Signal Classification , 2018, IEEE Wireless Communications Letters.

[17]  Timothy J. O'Shea,et al.  Applications of Machine Learning to Cognitive Radio Networks , 2007, IEEE Wireless Communications.

[18]  Samir S. Soliman,et al.  Signal classification using statistical moments , 1992, IEEE Trans. Commun..

[19]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[20]  Jiawei Zhu,et al.  Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges , 2018, IEEE Access.

[21]  Sofie Pollin,et al.  Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors , 2017, IEEE Transactions on Cognitive Communications and Networking.

[22]  Ekram Hossain,et al.  Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2013, IEEE Journal on Selected Areas in Communications.

[23]  B. Ramkumar,et al.  Automatic modulation classification for cognitive radios using cyclic feature detection , 2009, IEEE Circuits and Systems Magazine.

[24]  Asoke K. Nandi,et al.  Automatic Modulation Classification Using Combination of Genetic Programming and KNN , 2012, IEEE Transactions on Wireless Communications.

[25]  Elsayed Elsayed Azzouz,et al.  Algorithms for automatic modulation recognition of communication signals , 1998, IEEE Trans. Commun..

[26]  Ingrid Moerman,et al.  End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications , 2017, IEEE Access.

[27]  Timothy J. O'Shea,et al.  Deep architectures for modulation recognition , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[28]  Shu Liu,et al.  Automatic modulation classification of digital modulation signals with stacked autoencoders , 2017, Digit. Signal Process..

[29]  T. Charles Clancy,et al.  Convolutional Radio Modulation Recognition Networks , 2016, EANN.

[30]  Cheol-Sun Park,et al.  Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM , 2008, 2008 10th International Conference on Advanced Communication Technology.

[31]  Jie Yang,et al.  Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios , 2019, IEEE Transactions on Vehicular Technology.

[32]  Marko Höyhtyä,et al.  Spectrum Occupancy Measurements: A Survey and Use of Interference Maps , 2016, IEEE Communications Surveys & Tutorials.

[33]  Hongguang Li,et al.  Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles , 2018, Sensors.

[34]  Hüseyin Arslan,et al.  A survey of spectrum sensing algorithms for cognitive radio applications , 2009, IEEE Communications Surveys & Tutorials.

[35]  An-Yeu Wu,et al.  Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading , 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).