Abstract
Automatic modulation recognition (AMR) involves identifying the modulation of electromagnetic signals in a noncollaborative manner. Deep learning-based methods have become a focused research topic in the AMR field. Such models are frequently trained using standardized data, relying on many computational and storage resources. However, in real-world applications, the finite resources of edge devices limit the deployment of large-scale models. In addition, traditional networks cannot handle real-world signals of varying lengths and local missing data. Thus, we propose a network structure based on a convolutional Transformer with a dual-attention mechanism. This proposed structure effectively utilizes the inductive bias of the lightweight convolution and the global property of the Transformer model, thereby fusing local features with global features to get high recognition accuracy. Moreover, the model can adapt to the length of the input signals while maintaining strong robustness against incomplete signals. Experimental results on the open-source datasets RML2016.10a, RML2016.10b, and RML2018.01a demonstrate that the proposed network structure can achieve 95.05%, 94.79%, and 98.14% accuracy, respectively, with enhancement training and maintain greater than 90% accuracy when the signals are incomplete. In addition, the proposed network structure has fewer parameters and lower computational cost than benchmark methods.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The datasets used in this study are all open source and available at DeepSig.
Materials Availability
Not applicable.
Code Availability
Available when necessary.
References
Zhang X, Chen X, Wang Y, Gui G, Adebisi B, Sari H, Adachi F (2023) Lightweight automatic modulation classification via progressive differentiable architecture search. IEEE Trans Cogn Commun Netw 9(6):1519–1530
Zhang F, Luo C, Xu J et al (2022) Deep learning based automatic modulation recognition: Models, datasets, and challenges. Digit Signal Prog 129:103650
Zhu Z, Nandi AK (2015) Automatic modulation classification: principles, algorithms and applications. John Wiley & Sons
Dobre OA, Abdi A, Bar-Ness Y et al (2007) Survey of automatic modulation classification techniques: classical approaches and new trends. IET Commun 1(2):137–156
Wei W, Mendel JM (2000) Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Trans Commun 48(2):189–193
Polydoros A, Kim K (1990) On the detection and classification of quadrature digital modulations in broad-band noise. IEEE Trans Commun 38(8):1199–1211
Park CS, Jang W, Nah SP, et al (2007) Automatic modulation recognition using support vector machine in software radio applications. In: Proc IEEE 9th Int Conf Adv Commun Technol (ICACT), pp 9–12
Wu HC, Saquib M, Yun Z (2008) Novel automatic modulation classification using cumulant features for communications via multipath channels. IEEE Trans Wirel Commun 7(8):3098–3105
Lopatka J, Pedzisz M (2000) Automatic modulation classification using statistical moments and a fuzzy classifier. In: Proc IEEE 5th Int Conf Signal Process (ICSP), pp 1500–1506
Amodei D, Ananthanarayanan S, Anubhai R, et al (2016) Deep speech 2: End-to-end speech recognition in english and mandarin. In: Proc PMLR 5th Int Conf Mach Learn (ICML), pp 173–182
Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
O’Shea TJ, Corgan J, Clancy TC (2016) Convolutional radio modulation recognition networks. In: Proc Springer 17th Eng Appl Neural Netw (EANN), pp 213–226
O’shea TJ, West N (2016) Radio machine learning dataset generation with gnu radio. In: Proc GNU Conf
Chen Z, Cui H, Xiang J et al (2021) Signet: A novel deep learning framework for radio signal classification. IEEE Trans Cogn Commun Netw 8(2):529–541
Tekbıyık K, Ekti AR, Görçin A, et al (2020) Robust and fast automatic modulation classification with cnn under multipath fading channels. In: Proc IEEE 91st Veh Technol Conf (VTCSpring), pp 1–6
Xu J, Luo C, Parr G et al (2020) A spatiotemporal multi-channel learning framework for automatic modulation recognition. IEEE Wirel Commun Lett 9(10):1629–1632
Fran C, et al (2017) Xception: Deep learning with depth wise separable convolutions. In: Proc IEEE/CVF Conf Comput Vis Pattern Recog (CVPR), pp 1251–1258
Howard AG, Zhu M, Chen B, et al (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
Li J, Wen Y, He L (2023) Scconv: Spatial and channel reconstruction convolution for feature redundancy. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6153–6162
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process Syst (NIPS) 30
Devlin J, Chang MW, Lee K, et al (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805
Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: Trans Image Recogn Scale. arXiv:2010.11929
Wu H, Xiao B, Codella N, et al (2021) Cvt: Introducing convolutions to vision transformers. In: Proc IEEE/CVF Int Conf Comput Vis (ICCV), pp 22–31
Su H, Fan X, Liu H (2022) Robust and efficient modulation recognition with pyramid signal transformer. In: Proc IEEE Global Commun Conf, pp 1868–1874
Kumar Y, Sheoran M, Jajoo G et al (2020) Automatic modulation classification based on constellation density using deep learning. IEEE Commun Lett 24(6):1275–1278
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
Rajendran S, Meert W, Giustiniano D et al (2018) Deep learning models for wireless signal classification with distributed low-cost spectrum sensors. IEEE Trans Cogn Commun Netw 4(3):433–445
Hong D, Zhang Z, Xu X (2017) Automatic modulation classification using recurrent neural networks. In: Proc IEEE 3rd Int Conf Comput Commun (ICCC), pp 695–700
West NE, O’shea T (2017) Deep architectures for modulation recognition. In: Proc IEEE Int Symp Dyn Spectr Access Netw (DySPAN), pp 1–6
Xu J, Luo C, Parr G et al (2020) A spatiotemporal multi-channel learning framework for automatic modulation recognition. IEEE Wirel Commun Lett 9(10):1629–1632
Zhang F, Luo C, Xu J et al (2021) An efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation. IEEE Commun Lett 25(10):3287–3290
Njoku JN, Morocho-Cayamcela ME, Lim W (2021) Cgdnet: Efficient hybrid deep learning model for robust automatic modulation recognition. IEEE Networking Letters 3(2):47–51
Zhang J, Wang T, Feng Z, et al (2023a) Towards the automatic modulation classification with adaptive wavelet network. IEEE Trans Cogn Commun Netw 9(3):549–563
Zhang J, Wang T, Feng Z, et al (2023b) Amc-net: An effective network for automatic modulation classification. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1–5
Cai J, Gan F, Cao X et al (2022) Signal modulation classification based on the transformer network. IEEE Trans Cogn Commun Netw 8(3):1348–1357
Kong W, Yang Q, Jiao X, et al (2021) A transformer-based ctdnn structure for automatic modulation recognition. In: Proc IEEE 7th Int Conf Comput Commun (ICCC), pp 159–163
Kong W, Jiao X, Xu Y et al (2023) A transformer-based contrastive semi-supervised learning framework for automatic modulation recognition. IEEE Trans Cogn Commun Netw 9(4):950–962
Hamidi-Rad S, Jain S (2021) Mcformer: A transformer based deep neural network for automatic modulation classification. In: Proc IEEE Global Commun Conf, pp 1–6
Changxin F (2020) Principle of Communications. National Defense Industry Press
Liu F, Masouros C, Petropulu AP, et al (2020a) Joint radar and communication design: Applications, state-of-the-art, and the road ahead. IEEE Trans Commun 68(6):3834–3862
Liu F, Masouros C, Petropulu AP, et al (2020b) Joint radar and communication design: Applications, state-of-the-art, and the road ahead. IEEE Trans Commun 68(6):3834–3862
He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: Proc IEEE/CVF Conf Comput Vis Pattern Recog (CVPR), pp 770–778
Sifre L, Mallat S (2014) Rigid-motion scattering for texture classification. arXiv:1403.1687
Chefer H, Gur S, Wolf L (2021) Transformer interpretability beyond attention visualization. In: Proc IEEE/CVF Conf Comput Vis Pattern Recog (CVPR), pp 782–791
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
The authors contributed equally to this work.
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no conflict of interest.
Consent for Publication
Sincerely hope the publication.
Ethics Approval and Consent to Participate
Not applicable.
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.
About this article
Cite this article
Yi, Z., Meng, H., Gao, L. et al. Efficient convolutional dual-attention transformer for automatic modulation recognition. Appl Intell 55, 231 (2025). https://doi.org/10.1007/s10489-024-06202-6
Accepted:
Published:
DOI: https://doi.org/10.1007/s10489-024-06202-6