Deep Hierarchical Network for Automatic Modulation Classification

In non-cooperative communication scenarios, automatic modulation classification (AMC) is the premise of information acquisition. It has been a difficult issue for decades due to the attenuation and interference during wireless transmission. In this paper, a novel deep hierarchical network (DHN) based on convolutional neural network (CNN) is proposed for the AMC. The model is designed to combine the shallow features with high-level features. Thus, it can simultaneously have global receptive field and location information through multi-level feature extraction and does not require any transformation of the raw data. To make full use of limited data, a new method is proposed to use signal-to-noise ratio (SNR) as a weight in training instead of working as an indicator of system robustness. Furthermore, some other deep learning methods have been used to explore whether they could improve the performance of the proposed model. Several new techniques have been chosen to be applied in the DHN at last. Then, a detailed analysis of the improvement in network performance is provided. Combination of the DHN and the weighted-loss can achieve more than 93% classification accuracy which is the best performance in an open source dataset.

[1]  R. Mammone,et al.  A new method of modulation classification for digitally modulated signals , 1992, MILCOM 92 Conference Record.

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

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

[4]  Fenglin Fu,et al.  Digital modulation identification by wavelet analysis , 2005, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05).

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Sepp Hochreiter,et al.  Self-Normalizing Neural Networks , 2017, NIPS.

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

[8]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

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

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

[11]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[13]  Guan Gui,et al.  Deep Learning for an Effective Nonorthogonal Multiple Access Scheme , 2018, IEEE Transactions on Vehicular Technology.

[14]  Guan Gui,et al.  Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System , 2018, IEEE Transactions on Vehicular Technology.

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

[16]  Andreas Polydoros,et al.  Likelihood methods for MPSK modulation classification , 1995, IEEE Trans. Commun..

[17]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[19]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[20]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

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

[22]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  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.

[24]  S. S. Soliman,et al.  Automatic modulation recognition of digitally modulated signals , 1989, IEEE Military Communications Conference, 'Bridging the Gap. Interoperability, Survivability, Security'.

[25]  R. Mammone,et al.  Modulation classification using a neutral tree network , 1993, Proceedings of MILCOM '93 - IEEE Military Communications Conference.

[26]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

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

[29]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

[30]  Octavia A. Dobre,et al.  On the likelihood-based approach to modulation classification , 2009, IEEE Transactions on Wireless Communications.