• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 19, Issue 2, 235 (2021)
WU Nan*, GU Wanbo, and WANG Xudong
Author Affiliations
  • [in Chinese]
  • show less
    DOI: 10.11805/tkyda2020034 Cite this Article
    WU Nan, GU Wanbo, WANG Xudong. A novel efficient automatic modulation classification algorithm using deep LSTM aided convolutional networks[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(2): 235 Copy Citation Text show less
    References

    [1] DOBRE O A,ABDI A,BAR-NESS Y,et al. Survey of automatic modulation classification techniques:classical approaches and new trends[J]. IET Communications, 2007,1(2):137-156.

    [2] LI X,DONG F,ZHANG S,et al. A survey on deep learning techniques in wireless signal recognition[J]. Wireless Communications & Mobile Computing, 2019:1-12.

    [3] CHAVALI V G, SILVA C R C M D. Maximum-likelihood classification of digital amplitude-phase modulated signals in flat fading non-Gaussian channels[J]. IEEE Transactions on Communications, 2011,59(8):2051-2056.

    [4] BROWN R A,LAUZON M L,FRAYNE R. A general description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous s-transform spectrum nonredundantly[J]. IEEE Transactions on Signal Processing, 2010,58(1):281-290.

    [5] AZZOUZ E E,NANDI A K. Procedure for automatic recognition of analogue and digital modulations[J]. IEE Proceedings - Communications, 1996,143(5):259-266.

    [6] DOBRE O A,HAMEED F. Likelihood-based algorithms for linear digital modulation classification in fading channels[C]// 2006 Canadian Conference on Electrical and Computer Engineering. Ottawa,Ontario,Canada:IEEE, 2006.

    [7] MULLER F C B F,CARDOSO C,KLAUTAU A. A front end for discriminative learning in automatic modulation classification[J]. IEEE Communications Letters, 2011,15(4):443-445.

    [8] ABDELMUTALAB A,ASSALEH K,EL-TARHUNI M. Automatic modulation classification using polynomial classifiers[C]// IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC). Washington, DC,USA:IEEE, 2014:806-810.

    [9] AUBRY A,BAZZONI A,CAROTENUTO V,et al. Cumulants-based radar specific emitter identification[C]// IEEE International Workshop on Information Forensics and Security. Iguacu Falls,Brazil:IEEE, 2011:1-6.

    [10] KIM K,AKBAR I A,BAE K K,et al. Cyclostationary approaches to signal detection and classification in cognitive radio[C]// 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Dublin,Ireland:IEEE, 2007:212-215.

    [11] DOBRE O A,BAR-NESS Y,WEI S. Higher-order cyclic cumulants for high order modulation classification[C]// IEEE Military Communications Conference. Boston,MA,USA:IEEE, 2003:112-117.

    [12] XU Wei,YU Jianyu,CHEN Mao. Intra-pulse modulation recognition of radar signal based on multi-dimensional features[J]. Journal of Terahertz Science and Electronic Information Technology, 2018,16(1): 81-86.

    [13] REDMON J,DIVVALA S,GIRSHICK R,et al. You only look once: unified, real-time object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas,NV,USA:IEEE, 2016:779-788.

    [14] ZHU Jun-Yan,PARK Taesung,ISOLA Phillip,et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]// IEEE International Conference on Computer Vision (ICCV). Venice,Italy:IEEE, 2017:1-18.

    [15] ABDEL-HAMID O,MOHAMED A,JIANG H,et al. Convolutional neural networks for speech recognition[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014,22(10):1533-1545.

    [16] O'SHEA T J,CORGAN J,CLANCY T C. Convolutional radio modulation recognition networks[C]// International Conference on Engineering Applications of Neural Networks. Aberdeen,UK: Springer, 2016:213-226.

    [17] BA Lei Jimmy,CARUANA Rich. Do deep nets really need to be deep?[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge,MA,USA:MIT Press, 2014:2654–2662.

    [18] SIMONYAN Karen,ZISSERMA Andrew. Very deep convolutional networks for large-scale image recognition[C]// ICLR 2015. San Diego,CA,USA:[s.n.], 2015:1-14.

    [19] HE K M,ZHANG X Y,REN S Q,et al. Deep residual learning for image recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,USA:IEEE, 2016:770-778.

    [20] O'SHEA T J,ROY T,CLANCY T C. Over-the-air deep learning based radio signal classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2018,12(1):168-179.

    [21] HUANG G,LIU Z,MAATEN L V D,et al. Densely connected convolutional networks[C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu,HI,USA:IEEE, 2017:2261-2269.

    [22] RAMJEE Sharan,JU Shengtai,YANG Diyu,et al. Fast deep learning for automatic modulation classi.cation[J/OL]. arXiv, 2019:1901.05850v1.

    [23] HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997,9(8):1735-1780.

    [24] RAJENDRAN S,MEERT W,GIUSTINIANO D,et al. Deep learning models for wireless signal classification with distributed low-cost spectrum sensors[J]. IEEE Transactions on Cognitive Communications and Networking, 2018,4(3):433-445.

    [25] SAINATH T N,VINYALS O,SENIOR A,et al. Convolutional,long short-term memory,fully connected deep neural networks[C]//IEEE International Conference on Acoustics,Speech and Signal Processing. Brisbane,QLD,Australia:IEEE, 2015:4580-4584.

    [26] TANG B,TU Y,ZHANG Z Y,et al. Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks[J]. IEEE Access, 2018(6):15713-15722.

    [27] TU Y,LIN Y,WANG J,et al. Semi-supervised learning with generative adversarial networks on digital signal modulation classification[J]. Computers, Materials and Continua, 2018,55(2):242-254.

    [28] LI M,LIU G,LI S,et al. Radio classify generative adversarial networks: a semi-supervised method for modulation recognition[C]// 18th International Conference on Communication Technology. Chongqing,China:IEEE, 2018:669-672.

    [29] O'SHEA T,WEST N. Radio machine learning dataset generation with GNU radio[C]// Proceedings of the GNU Radio Conference. Boulder,Colorado,USA:[s.n.], 2016.

    [30] KULIN M,KAZAZ T,MOERMAN I,et al. End-to-end learning from spectrum data: a deep learning approach for wireless signal identification in spectrum monitoring applications[J]. IEEE Access, 2018(6):18484-18501.

    [31] GOODFELLOW I,BENGIO Y,COURVILLE A. Deep learning[M]. Cambridge,MA,USA:MIT Press, 2017:330-371.

    [32] KLAMBAUER G,UNTERTHINER T,MAYR A,et al. Self-normalizing neural networks[C]// Proceedings of 31st Conference on Neural Information Processing Systems(NIPS 2017). Long Beach,CA,USA:Springer, 2017:971-980.

    [33] GRAVES Alex. Supervised sequence labelling with recurrent neural networks[M]. Berlin,Heidelberg:Springer, 2012:31-38.

    [34] SUTSKEVER I,VINYALS O,LE Q V. Sequence to sequence learning with neural networks[C]// Proceedings of the 27th International Conference on Neural Information Processing System(NIPS 2014). Cambridge,MA,USA:MIT Press, 2014: 3104-3112.

    WU Nan, GU Wanbo, WANG Xudong. A novel efficient automatic modulation classification algorithm using deep LSTM aided convolutional networks[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(2): 235
    Download Citation