Skip to main content
SpringerLink
Log in

CSCNN: Lightweight Modulation Recognition Model for Mobile Multimedia Intelligent Information Processing

  • Research
  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

A new round of global technological revolution and industrial transformation is accelerating. The era of 6G is on the horizon, envisioning the creation of an integrated network spanning across regions, airspace, seas, and land. This integration aims to achieve a truly global seamless coverage. Simultaneously, the importance of modulation recognition technology is continuously growing in tandem with the advancements in communication technology during this 6G vision. But faced with the vast amount of electromagnetic data and limited computing resources of terminal devices, previous technologies were unable to meet the realtime decision-making requirements for processing short observations or sudden bursts of signals in deployable systems. In this paper, to better leverage the correlation in the I/Q data of communication signals, our approach proposes the Deep Complex Separable Convolution (DCSC) operation by combining separable convolution operation and complex convolution operation. At the same time, to better preserve coupling information between channels and minimize the model size, we propose the Multilevel Separable Convolutional Residual Block (MSCRB). Based on the above two methods, we constructed the Complex Separable Convolutional Neural Network (CSCNN). This neural network significantly reduces the complexity of the deep learning model. We conducted experiments on RML2016.10a dataset and a dataset we created using signals collected through software-defined radio platform. On the RML2016 dataset, the smallest network we constructed, CSCNN-Tiny, has a model size of 3.04M, only 24.6% of the size of MobileNet. With 1.361M Flops, only 6% of MobileNet. However, it achieved a recognition accuracy of 52.45%, which is 0.54% higher than MobileNet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Algorithm 1
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

No datasets were generated or analysed during the current study.

References

  1. Lin Y, Zhao H, Ma X, Tu Y, Wang M (2020) Adversarial attacks in modulation recognition with convolutional neural net-works. IEEE Trans Reliab 70(1):389–401

    Article  Google Scholar 

  2. Ma X, Shi H, Miao X, Li Q, Wang X, Ding L, Zhang H, Dai K (2023) Multiple dynamic impact signal identification method based on lightweight neural network with acceleration sensor. IEEE Sens J 23(15):17289–17300

    Article  Google Scholar 

  3. Yao Z, Fu X, Guo L, Wang Y, Lin Y, Shi S, Gui G (2023) Few-shot specific emitter identification using asymmetric masked auto-encoder. IEEE Commun Lett 27(10):2657–2661

    Article  Google Scholar 

  4. Lin Y, Wang M, Zhou X, Ding G, Mao S (2020) Dynamic spectrum interaction of UAV flight formation communication with priority: A deep reinforcement learning approach. IEEE Trans Cogn Commun Netw 6(3):892–903

    Article  Google Scholar 

  5. Tu Y, Lin Y, Wang J, Kim JU (2018) Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modu-lation Classification. Computers, Materials and Continua. 55(2):243–254

    Google Scholar 

  6. Yang C, Wang Y, Zhang J, Zhang H, Wei Z, Lin Z, Yuille A (2022) Lite vision transformer with enhanced self-attention. Pro-ceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp11998–12008

  7. Lin Y, Tu Y, Dou Z, Chen L, Mao S (2020) Contour stella image and deep learning for signal recognition in the physical layer. IEEE Trans Cogn Commun Netw 7(1):34–46

    Article  Google Scholar 

  8. Zhang S, Yang Y, Zhou Z, Sun Z, Lin Y (2024) DIBAD: A Disentangled Information Bottleneck Adversarial Defense Method us-ing Hilbert-Schmidt Independence Criterion for Spectrum Security. IEEE Trans Inform Forensic Sec, early access. https://doi.org/10.1109/TIFS.2024.3372798

    Article  Google Scholar 

  9. Tu Y, Lin Y, Hou C, Mao S (2020) Complex-valued networks for automatic modulation classification. IEEE Trans Ve-hicular Technol 69(9):10085–10089

    Article  Google Scholar 

  10. Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J (2019) Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE 107(8):1738–1762

    Article  Google Scholar 

  11. Lin Y, Zha H, Tu Y, Zhang S, Yan W, Xu C (2023) GLR-SEI: Green and low resource specific emitter identification based on complex networks and Fisher pruning. IEEE Trans Emerg Top Comput Intell, early access,. https://doi.org/10.1109/TETCI.2023.3303092

    Article  Google Scholar 

  12. Goudarzi M, Wu H, Palaniswami M, Buyya R (2020) An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans Mob Comput 20(4):1298–1311

    Article  Google Scholar 

  13. Bao Z, Lin Y, Zhang S, Li Z, Mao S (2021) Threat of adversarial attacks on DL-based IoT device identification. IEEE Internet Things J 9(11):9012–9024

    Article  Google Scholar 

  14. Chen J, Ran X (2019) Deep learning with edge computing: A review. Proceedings of the IEEE 107(8):1655–1674

    Article  Google Scholar 

  15. Liu C, Fu X, Wang Y, Guo L, Liu Y, Lin Y, Zhao H, Gui G (2023) Overcoming data limitations: a few-shot specific emitter identification method using self-supervised learning and adversarial augmentation. IEEE Trans Inform Forensics Secur 19:500–513

    Article  Google Scholar 

  16. Lin Y, Tu Y, Dou Z (2020) An improved neural network pruning technology for automatic modulation classification in edge de-vices. IEEE Trans Veh Technol 69(5):5703–5706

    Article  Google Scholar 

  17. Song M, Lou L, Chen X, Zhao X, Hong Y, Zhang S, He W (2023) Wi-LADL: A Wireless-Based Lightweight Attention Deep Learning Method for Human?Vehicle Recognition. IEEE Sens J 23(3):2803–2814

    Article  Google Scholar 

  18. Wang S, Tuor T, Salonidis T, Leung KK, Makaya C, He T, Chan K (2019) Adaptive federated learning in resource constrained edge computing systems. IEEE J Sel Areas Commun 37(6):1205–1221

    Article  Google Scholar 

  19. Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks. Proceedings of the IEEE/CVF conference on computer vision and pat-tern recognition (CVPR), pp 510–519

  20. Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning Transferable Architectures for Scalable Image Recognition. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 8697–8710

  21. Ma N, Zhang X, Zheng H T, Sun J (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture. Proceedings of the Euro-pean conference on computer vision (ECCV), pp 116–131

  22. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. Proceed-ings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4510–4520

  23. Huang G, Liu Z, Maaten L, Weinberger K (2017) Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4700–4708

  24. Xie S, Girshick R, Dollr P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 1492–1500

  25. O’shea TJ, West N (2016) Radio machine learning dataset generation with gnu radio. Proceedings of the GNU radio conference, 1(1)

Download references

Author information

Authors and Affiliations

  1. ChengDu Fuyuanchen Technology Co, Ltd, Chengdu, 610037, China

    Jun Chen, Yiping Huang & Ling Zhang

  2. No. 8511 Research Institute of China Aerospace Science and Industry Corporation, Nanjing, 210007, China

    Guangzhen Si

  3. Wuhan University, Wuhan, 430072, China

    Juzhen Wang

Authors
  1. Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiping Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guangzhen Si
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juzhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jun Chen is responsible for determining the topic and writing part of the paper. Yiping Huang is responsible for algorithm design and logic. Ling Zhang is responsible for language expression and beautification of figures and tables. Guangzhen Si is responsible for the experimental code design and actual implementation. Juzhen Wang is responsible for background research and part of the writing work.

Corresponding authors

Correspondence to Guangzhen Si or Juzhen Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Huang, Y., Zhang, L. et al. CSCNN: Lightweight Modulation Recognition Model for Mobile Multimedia Intelligent Information Processing. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02317-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-024-02317-9

Keywords

Search

Navigation