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A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT

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Abstract

Automatic modulation classification (AMC) is a challenging topic in the development of cognitive radio, which can sense and learn surrounding electromagnetic environments and help to make corresponding decisions. In this paper, we propose to complete the real-time AMC through constructing a lightweight neural network MobileViT driven by the clustered constellation images. Firstly, the clustered constellation images are transformed from I/Q sequences to help extract robust and discriminative features. Then the lightweight neural network called MobileViT is developed for the real-time constellation image classification. Experimental results on the public dataset RadioML 2016.10a with edge computing platform demonstrate the superiority and efficiency of MobileViT. Furthermore, the extensive ablation tests prove the robustness of the proposed method to the learning rate and batch size. To the best of our knowledge, this is the first attempt to deploy the deep learning model to complete the real-time classification of modulation schemes of received signals at the edge.

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References

  • Akyön FC, Alp Y, Gök G, Arikan O (2018) Deep learning in electronic warfare systems: automatic intra-pulse modulation recognition. Paper presented at IEEE 26th signal processing and communications applications conference (SIU), Izmir, Turkey, pp. 1–4

  • Chung WH (2013) Sequential likelihood ratio test under incomplete signal model for spectrum sensing. IEEE Trans Wirel Commun 12(2):494–503

    Article  Google Scholar 

  • Dong B et al (2022) A lightweight decentralized learning-based automatic modulation classification method for resource-constrained edge devices. IEEE Internet Things J early access 9(24):24708–24720

    Article  Google Scholar 

  • Fu X et al (2021) Lightweight automatic modulation classification based on decentralized learning. IEEE Trans Cogni Commun Netw 8(1):57–70

    Article  Google Scholar 

  • Gui G, Liu M, Tang F, Kato N, Adachi F (2020) 6G: Opening new horizons for integration of comfort, security, and intelligence. IEEE Wirel Commun 27(5):126–132

    Article  Google Scholar 

  • Hermawan AP, Ginanjar RR, Kim DS, Lee J (2020) CNN-based automatic modulation classification for beyond 5G communications. IEEE Commun Lett 24(5):1038–1041

    Article  Google Scholar 

  • Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. Int conf mach learn PMLR 37:448–456

    Google Scholar 

  • Jais I, Ismail A, Nisa S (2019) Adam optimization algorithm for wide and deep neural network. Knowl Eng Data Sci 2(1):41–46

    Article  Google Scholar 

  • Jajoo G, Kumar Y, Yadav S (2019) Blind signal PSK/QAM recognition using clustering analysis of constellation signature in flat fading channel. IEEE Commun Lett 23(10):1853–1856

    Article  Google Scholar 

  • Ke Z, Vikalo H (2021) Real-time radio technology and modulation classification via an LSTM auto-encoder. IEEE Trans Wirel Commun 21(1):370–382

    Article  Google Scholar 

  • Kim S, Moon C, Kim J, Kim D (2021) A hybrid deep learning model for automatic modulation classification. IEEE Wirel Commun Lett 11(2):313–317

    Article  Google Scholar 

  • Kumar Y, Sheoran M, Jajoo G, Yadav S (2020) Automatic modulation classification based on constellation density using deep learning. IEEE Commun Lett 24(6):1275–1278

    Article  Google Scholar 

  • Lee J, Kim K, Shin Y (2019) Feature image-based automatic modulation classification method using CNN algorithm. In: IEEE international conference on artificial intelligence in information and communication (ICAIIC), Okinawa, Japan, pp 1–4

  • Lin Y, Tu Y, Dou Z, Chen L, Mao S (2021) Contour stella image and deep learning for signal recognition in the physical layer. IEEE Trans Cognit Commun Netw 7(1):34–46

    Article  Google Scholar 

  • Liu Y, Yang M, Li J, Zheng Q, Wang D (2020) Dynamic hand gesture recognition using 2D convolutional neural network. Eng Lett 28(1):243–254

    Google Scholar 

  • Luan S, Gao Y, Zhou J, Zhang Z (2021) Automatic modulation classification based on Cauchy-score constellation and lightweight network under impulsive noise. IEEE Wirel Commun Lett 10(11):2509–2513

    Article  Google Scholar 

  • Ma H et al (2020) Cross model deep learning scheme for automatic modulation classification. IEEE Access 8:78923–78931

    Article  Google Scholar 

  • Mehta S, Rastegari M (2022) Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. In:international conference on learning representations (ICLR), pp 1–26

  • O’Shea TJ, Roy T, Clancy TC (2018) Over-the-air deep learning based radio signal classification. IEEE J Sel Top Signal Process 12(1):168–179

    Article  Google Scholar 

  • Peng S et al (2018) Modulation classification based on signal constellation diagrams and deep learning. IEEE Trans Neural Netw Learn Syst 30(3):718–727

    Article  PubMed  Google Scholar 

  • Peng S, Sun S, Yao Y (2021) A survey of modulation classification using deep learning: Signal representation and data preprocessing. IEEE Trans Neural Netw Learn Syst 33(12):7020–7038

    Article  Google Scholar 

  • Roy C et al (2021) An ensemble deep learning model for automatic modulation classification in 5G and beyond IoT networks. Comput Intell Neurosci 2021(5047355):1–8

    Article  Google Scholar 

  • Sandler M, Howard A, Zhu M, Chen L (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, USA, pp 4510–4520

  • Shi Y, Hua X, Lei J, Zisen Q (2022) ConvLSTMAE: a spatiotemporal parallel autoencoders for automatic modulation classification. IEEE Commun Lett 26(8):1804–1808

    Article  Google Scholar 

  • Shimbo D, Oka I (2010) A modulation classification using amplitude moments in OFDM systems. In: International Symposium on Information Theory & Its Applications, Taichung, Taiwan, pp 288-293

  • Sun Y, Ball EA (2022) Automatic modulation classification using techniques from image classification. IET Commun 16(11):1303–1314

    Article  Google Scholar 

  • Wang Y, Liu M, Yang J, Gui G (2019) Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Trans Veh Technol 68(4):4074–4077

    Article  Google Scholar 

  • Wang Y, Yang J, Liu M, Gui G (2020a) LightAMC: lightweight automatic modulation classification via deep learning and compressive sensing. IEEE Trans Veh Technol 69(3):3491–3495

    Article  Google Scholar 

  • Wang Y et al (2020b) Distributed learning for automatic modulation classification in edge devices. IEEE Wirel Commun Lett 9(12):2177–2181

    Article  Google Scholar 

  • Wang D et al (2021) Multiple high-order cumulants-based spectrum sensing in full-duplex-enabled cognitive IoT networks. IEEE Internet Things J 8(11):9330–9343

    Article  Google Scholar 

  • Xu Y, Xu G, Ma C (2022) A novel blind high-order modulation classifier using accumulated constellation temporal convolution for OSTBC-OFDM systems. IEEE Trans Circuits Syst II Express Briefs 10(11):2509–2513

    Google Scholar 

  • Yan X, Feng G, Wu H-C, Xiang W, Wang Q (2017) Innovative robust modulation classification using graph-based cyclic-spectrum analysis. IEEE Commun Lett 21(1):16–19

    Article  Google Scholar 

  • Yan X, Zhang G, Wu H (2018) A novel automatic modulation classifier using graph-based constellation analysis for M-ary QAM. IEEE Commun Lett 23(2):298–301

    Article  Google Scholar 

  • You L, et al. (2022) GPU-accelerated faster mean shift with euclidean distance metrics. In: IEEE 46th annual computers, software, and applications conference (COMPSAC),Los Alamitos, USA, pp 211-216

  • Zhang Z, Wang C, Gan C, Sun S, Wang M (2019) Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD. IEEE Trans Signal Inf Process over Netw 5(3):469–478

    Article  Google Scholar 

  • Zhang Z, Luo H, Wang C, Gan C, Xiong Y (2020) Automatic modulation classification using CNN-LSTM based dual-stream structure. IEEE Trans Veh Technol 69(11):13521–13531

    Article  Google Scholar 

  • Zhang X et al (2022) NAS-AMR: neural architecture search-based automatic modulation recognition for integrated sensing and communication systems. IEEE Trans Cognit Commun Netw 8(3):1374–1386

    Article  Google Scholar 

  • Zhao M, Chang CH, Xie W, Xie Z, Hu J (2020) “Cloud shape classification system based on multi-channel CNN and improved fdm. IEEE Access 8:44111–44124

    Article  Google Scholar 

  • Zheng J, Lv Y (2018) Likelihood-based automatic modulation classification in OFDM with index modulation”. IEEE Trans Veh Technol 67(9):8192–8204

    Article  Google Scholar 

  • Zheng Q, Yang M, Yang J, Zhang Q, Zhang X (2018) Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process. IEEE Access 6:15844–15869

    Article  Google Scholar 

  • Zheng Q, Tian X, Yang M, Su H (2020a) CLMIP: cross-layer manifold invariance based pruning method of deep convolutional neural network for real-time road type recognition. Multidimens Syst Signal Process 32(1):239–262

    Article  Google Scholar 

  • Zheng Q et al (2020b) PAC-bayesian framework based drop-path method for 2D discriminative convolutional network pruning. Multidimens Syst Signal Process 31(3):793–827

    Article  Google Scholar 

  • Zheng Q, Zhao P, Zhang D, Wang H (2021) MR-DCAE: manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification. Int J Intell Syst 36(12):7204–7238

    Article  Google Scholar 

  • Zheng Q, Zhao P, Wang H, Elhanashi A, Saponara S (2022) Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation. IEEE Commun Lett 26(6):1298–1302

    Article  Google Scholar 

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Acknowledgements

This research was supported by Shandong Provincial Natural Science Foundation, grant number ZR2023QF125.

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Authors and Affiliations

  1. School of Intelligent Engineering, Shandong Management University, Jinan, 250357, China

    Qinghe Zheng, Xinyu Tian & Zhiguo Yu

  2. Department of Information Engineering, University of Pisa, 56122, Pisa, Italy

    Sergio Saponara & Abdussalam Elhanashi

  3. Department of Electrical and Computer Engineering, University of Kentucky, Lexington, 40503, USA

    Rui Yu

Authors
  1. Qinghe Zheng
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  2. Sergio Saponara
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  3. Xinyu Tian
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  4. Zhiguo Yu
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  5. Abdussalam Elhanashi
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  6. Rui Yu
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Correspondence to Qinghe Zheng.

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Zheng, Q., Saponara, S., Tian, X. et al. A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT. Cogn Neurodyn 18, 659–671 (2024). https://doi.org/10.1007/s11571-023-10015-7

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  • DOI: https://doi.org/10.1007/s11571-023-10015-7

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