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To effectively address this shortage, automatic modulation classification (AMC) has emerged as one of the critical factors. Most existing deep learning\u2010based AMC methods rely on supervised attention models. However, these approaches have not fully accounted for the inherent characteristics of modulation signals and feature sparsity. In response, this paper proposes a weight non\u2010negative constraint recurrent self\u2010attention (WNRSA) model. This model incorporates a recurrent attention module (RAM) within an autoencoder architecture, creating a recurrent self\u2010attention extraction mechanism that enhances multi\u2010dimensional feature representations. RAM comprises three types of attention modules: spatial, frequency, and temporal. The point attention model (PAM) extracts local spatial information to emphasize critical regions in the image. The frequency attention model (FAM) captures salient features at different scales in the frequency domain, reducing noise sensitivity to details and high\u2010frequency information. The time attention model (TAM) captures temporal information, strengthening the ability to extract dynamic features. Additionally, we introduce weight non\u2010negative constraint and KL\u2010divergence regularization term to optimize the WNRSA model's loss function, achieving sparser feature representations and reducing sensitivity to noise. Experimental results demonstrate that the WNRSA model achieves superior performance across various signal\u2010to\u2010noise ratio (SNR)\u00a0levels.<\/jats:p>","DOI":"10.1049\/cmu2.70025","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T06:02:32Z","timestamp":1742277752000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic Modulation Classification via Recurrent Self\u2010Attention with Weight Non\u2010Negative Constraint"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0656-5101","authenticated-orcid":false,"given":"Shilong","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science Inner Mongolia University Hohhot China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2759-182X","authenticated-orcid":false,"given":"Yu","family":"Song","sequence":"additional","affiliation":[{"name":"College of Computer Science Inner Mongolia University Hohhot China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shubin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering Inner Mongolia University Hohhot China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,3,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010896"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1049\/cmu2.12588"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2025.3527901"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1049\/cmu2.12856"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1049\/cmu2.12608"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3423018"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3281896"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3431236"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2024.3433326"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1049\/cmu2.12682"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2971001"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3030018"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3091523"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1049\/cmu2.12823"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2024.3412234"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2022.3140828"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2983143"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2022.3162422"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3238995"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCOMM.2023.3271633"},{"issue":"6","key":"e_1_2_10_22_1","first-page":"3002","article-title":"An Advancing Temporal Convolutional Network for 5G Latency Services via Automatic Modulation Recognition","volume":"69","author":"Xu Y.","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems II: Express Briefs"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2022.3179450"},{"key":"e_1_2_10_24_1","doi-asserted-by":"crossref","unstructured":"X.Zhang S.Ma J.Shi P.Li andG.Yue \u201cAutomatic Modulation Recognition Based on Multi\u2010Channel Neural Network Model \u201d inInternational Conference on Wireless Communications and Signal Processing (WCSP)(IEEE 2022) 01\u201305.","DOI":"10.1109\/WCSP55476.2022.10039264"},{"key":"e_1_2_10_25_1","doi-asserted-by":"crossref","unstructured":"X.Wu S.Wei andY.Zhou \u201cDeep Multi\u2010Scale Representation Learning with Attention for Automatic Modulation Classification \u201d inInternational Joint Conference on Neural Networks (IJCNN)(IEEE 2022) 1\u20138.","DOI":"10.1109\/IJCNN55064.2022.9892813"},{"key":"e_1_2_10_26_1","doi-asserted-by":"crossref","unstructured":"A.Parmar D. 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