An Open-Sourced Time-Frequency Domain RF Classification Framework

In this paper we present a machine learning-based approach to solving the radio-frequency (RF) signal classification problem in a data-driven way. To this end, we propose an efficient and easy-to-use graphical user interface (GUI) for researchers to collect their own data to build a customized RF classification system. The GUI operates in the time-frequency (TF) domain, which is achieved by applying short-time Fourier transform to the in-phase and quadrature (IQ) time domain signals. Using the proposed GUI, a radio frequency (RF) dataset is collected from the ultra high frequency industrial, scientific, and medical (ISM) bands using commercial-off-the-shelf (COTS) transceivers, and COTS transceiver modules. We train three different variants of convolutional neural network models, such as VGG and ResNet, using the collected dataset and show that they can perform acceptable test-time classification (up to 95% accuracy) on unseen real-world RF signal recordings. Our experimental results also show that a carefully prepared TF domain without a loss of information can achieve better performance than a magnitude-only representation that loses phase information during the TF transformation. We open-source our project to provide the public with access to the labeled datasets, programming code, and the GUI software that can expedite the labeling process.

[1]  Sandeep Kumar Yadav,et al.  Blind Signal Modulation Recognition through Density Spread of Constellation Signature , 2020, Wirel. Pers. Commun..

[2]  Mark Turner,et al.  The software communications architecture: two decades of software radio technology innovation , 2015, IEEE Communications Magazine.

[3]  Jacques Palicot,et al.  Software radio: a catalyst for wireless innovation , 2015, IEEE Communications Magazine.

[4]  Tim O'Shea,et al.  Learning robust general radio signal detection using computer vision methods , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[5]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[6]  Joseph Mitola,et al.  The software radio architecture , 1995, IEEE Commun. Mag..

[7]  Kenichi Okada,et al.  Wireless Devices Identification with Light-Weight Convolutional Neural Network Operating on Quadrant IQ Transition Image , 2020, 2020 18th IEEE International New Circuits and Systems Conference (NEWCAS).

[8]  Alexander M. Wyglinski,et al.  Revolutionizing software defined radio: case studies in hardware, software, and education , 2016, IEEE Communications Magazine.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  R. W. Jones.  The International Telecommunication Union , 1997 .

[11]  Michael R. Souryal,et al.  Deep Learning Classification of 3.5-GHz Band Spectrograms With Applications to Spectrum Sensing , 2018, IEEE Transactions on Cognitive Communications and Networking.

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

[13]  H. D. Griffiths.  Detecting and Classifying Low Probability of Intercept Radar – Second edition . P. E. Pace Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2009. 857pp. + diskette Illustrated. £100, ISBN 978-1-59693-234-0. , 2011 .

[14]  Timothy J. O'Shea,et al.  SigMF: The Signal Metadata Format , 2018 .

[15]  Ruolin Zhou,et al.  SDR Demonstration of Signal Classification in Real-Time Using Deep Learning , 2019, 2019 IEEE Globecom Workshops (GC Wkshps).

[16]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[17]  Yi Shi,et al.  Deep Learning for Wireless Communications , 2019, Development and Analysis of Deep Learning Architectures.

[18]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[19]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[20]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[21]  Sarat Kumar Patra,et al.  WiST ID—Deep Learning-Based Large Scale Wireless Standard Technology Identification , 2020, IEEE Transactions on Cognitive Communications and Networking.

[22]  J.B. Allen,et al.  A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).