{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:51:39Z","timestamp":1783183899807,"version":"3.54.6"},"reference-count":21,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2025,11,1]]},"DOI":"10.1587\/transfun.2025eal2032","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T18:07:03Z","timestamp":1745258823000},"page":"1571-1574","source":"Crossref","is-referenced-by-count":2,"title":["A Lightweight Transformer for Automatic Modulation Recognition Based on Wavelet Convolution at the Low SNR"],"prefix":"10.1587","volume":"E108.A","author":[{"given":"Liliang","family":"ZHOU","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengrui","family":"YI","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"LUO","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengchun","family":"ZHOU","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] X. Zhang, H. Zhao, H. Zhu, B. Adebisi, G. Gui, H. Gacanin, and F. Adachi, \u201cNAS-AMR: Neural architecture search-based automatic modulation recognition for integrated sensing and communication systems,\u201d IEEE Trans. Cogn. Commun. Netw., vol.8, no.3, pp.1374-1386, 2022. 10.1109\/tccn.2022.3169740","DOI":"10.1109\/TCCN.2022.3169740"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] F. Zhang, C. Luo, J. Xu, Y. Luo, and F.C. Zheng, \u201cDeep learning based automatic modulation recognition: Models, datasets, and challenges,\u201d Digit. Signal Prog., vol.129, p.103650, 2022. 10.1016\/j.dsp.2022.103650","DOI":"10.1016\/j.dsp.2022.103650"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] J. Li, Y. Wen, and L. He, \u201cSCConv: Spatial and channel reconstruction convolution for feature redundancy,\u201d Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recog. (CVPR), pp.6153-6162, 2023. 10.1109\/cvpr52729.2023.00596","DOI":"10.1109\/CVPR52729.2023.00596"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] N.E. West and T. O\u2019Shea, \u201cDeep architectures for modulation recognition,\u201d Proc. IEEE Int. Symp. Dyn. Spectr. Access Netw. (DySPAN), pp.1-6, IEEE, 2017. 10.1109\/dyspan.2017.7920754","DOI":"10.1109\/DySPAN.2017.7920754"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] D. Hong, Z. Zhang, and X. Xu, \u201cAutomatic modulation classification using recurrent neural networks,\u201d Proc. IEEE 3rd Int. Conf. Comput. Commun. (ICCC), pp.695-700, IEEE, 2017. 10.1109\/compcomm.2017.8322633","DOI":"10.1109\/CompComm.2017.8322633"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, and S. Pollin, \u201cDeep learning models for wireless signal classification with distributed low-cost spectrum sensors,\u201d IEEE Trans. Cogn. Commun. Netw., vol.4, no.3, pp.433-445, 2018. 10.1109\/tccn.2018.2835460","DOI":"10.1109\/TCCN.2018.2835460"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] K. Tekb\u0131y\u0131k, A.R. Ekti, A. G\u00f6r\u00e7in, G.K. Kurt, and C. Ke\u00e7eci, \u201cRobust and fast automatic modulation classification with CNN under multipath fading channels,\u201d Proc. IEEE 91st Veh. Technol. Conf. (VTCSpring), pp.1-6, IEEE, 2020. 10.1109\/vtc2020-spring48590.2020.9128408","DOI":"10.1109\/VTC2020-Spring48590.2020.9128408"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] F. Zhang, C. Luo, J. Xu, and Y. Luo, \u201cAn efficient deep learning model for automatic modulation recognition based on parameter estimation and transformation,\u201d IEEE Commun. Lett., vol.25, no.10, pp.3287-3290, 2021. 10.1109\/lcomm.2021.3102656","DOI":"10.1109\/LCOMM.2021.3102656"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] S. Hamidi-Rad and S. Jain, \u201cMCformer: A transformer based deep neural network for automatic modulation classification,\u201d Proc. IEEE Global Commun. Conf., pp.1-6, IEEE, 2021. 10.1109\/globecom46510.2021.9685815","DOI":"10.1109\/GLOBECOM46510.2021.9685815"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] F. Chollet, \u201cXception: Deep learning with depthwise separable convolutions,\u201d Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recog. (CVPR), pp.1251-1258, 2017. 10.1109\/cvpr.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] M. Garg, D. Ghosh, and P.M. Pradhan, \u201cGestFormer: Multiscale wavelet pooling transformer network for dynamic hand gesture recognition,\u201d Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recog. (CVPR), pp.2473-2483, 2024. 10.1109\/cvprw63382.2024.00254","DOI":"10.1109\/CVPRW63382.2024.00254"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] S.E. Finder, R. Amoyal, E. Treister, and O. Freifeld, \u201cWavelet convolutions for large receptive fields,\u201d Proc. Eur. Conf. Comput. Vis. (ECCV), pp.363-380, Springer, 2024. 10.1007\/978-3-031-72949-2_21","DOI":"10.1007\/978-3-031-72949-2_21"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] G. Xu, W. Liao, X. Zhang, C. Li, X. He, and X. Wu, \u201cHaar wavelet downsampling: A simple but effective downsampling module for semantic segmentation,\u201d Pattern Recog., vol.143, p.109819, 2023. 10.1016\/j.patcog.2023.109819","DOI":"10.1016\/j.patcog.2023.109819"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] J. Zhang, T. Wang, Z. Feng, and S. Yang, \u201cToward the automatic modulation classification with adaptive wavelet network,\u201d IEEE Trans. Cogn. Commun. Netw., vol.9, no.3, pp.549-563, 2023. 10.1109\/tccn.2023.3252580","DOI":"10.1109\/TCCN.2023.3252580"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] Z. Yi, H. Meng, L. Gao, Z. He, and M. Yang, \u201cEfficient convolutional dual-attention transformer for automatic modulation recognition,\u201d Appl. Intell., vol.55, no.3, p.231, 2025. 10.1007\/s10489-024-06202-6","DOI":"10.1007\/s10489-024-06202-6"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] T.J. O\u2019Shea, J. Corgan, and T.C. Clancy, \u201cConvolutional radio modulation recognition networks,\u201d Proc. Springer 17th Eng. Appl. Neural Netw. (EANN), pp.213-226, Springer, 2016. 10.1007\/978-3-319-44188-7_16","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"17","unstructured":"[17] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Adv. Neural Inf. Process. Syst. (NIPS), vol.30, 2017."},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang, \u201cCvT: Introducing convolutions to vision transformers,\u201d Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV), pp.22-31, 2021. 10.1109\/iccv48922.2021.00009","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] N. Rashvand, K. Witham, G. Maldonado, V. Katariya, N. Marer Prabhu, G. Schirner, and H. Tabkhi, \u201cEnhancing automatic modulation recognition for IoT applications using transformers,\u201d IoT, vol.5, no.2, pp.212-226, 2024. 10.3390\/iot5020011","DOI":"10.3390\/iot5020011"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] S. Zhong, W. Wen, and J. Qin, \u201cSPEM: Self-adaptive pooling enhanced attention module for image recognition,\u201d Int. Conf. Multimed. Model., pp.41-53, Springer, 2023. 10.1007\/978-3-031-27818-1_4","DOI":"10.1007\/978-3-031-27818-1_4"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] T. Chen, S. Zheng, K. Qiu, L. Zhang, Q. Xuan, and X. Yang, \u201cAugmenting radio signals with wavelet transform for deep learning-based modulation recognition,\u201d IEEE Trans. Cogn. Commun. Netw., vol.10, no.6, pp.2029-2044, 2024. 10.1109\/tccn.2024.3400525","DOI":"10.1109\/TCCN.2024.3400525"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/11\/E108.A_2025EAL2032\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T03:39:25Z","timestamp":1761968365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/11\/E108.A_2025EAL2032\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":21,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2025eal2032","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"article-number":"2025EAL2032"}}