{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T16:57:50Z","timestamp":1762361870732,"version":"build-2065373602"},"reference-count":33,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":213,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014206","name":"National Key Laboratory Foundation of China","doi-asserted-by":"publisher","award":["6142411132203","2022-JCJQ-LB-006"],"award-info":[{"award-number":["6142411132203","2022-JCJQ-LB-006"]}],"id":[{"id":"10.13039\/501100014206","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101441"],"award-info":[{"award-number":["62101441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005232","name":"CAST Innovation Foundation","doi-asserted-by":"publisher","award":["Y23-WYHXJS-07"],"award-info":[{"award-number":["Y23-WYHXJS-07"]}],"id":[{"id":"10.13039\/501100005232","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Signal Processing"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Automatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal\u2010to\u2010noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventional recognition methods will deteriorate or even fail. To address these challenges, deep learning\u2010based modulation mode recognition technique is investigated in this paper for low\u2010speed asynchronous sampled signals under channel conditions with varying SNR and delay. Firstly, the low\u2010speed asynchronous sampled signals are modeled, and their in\u2010phase quadrature components are used to generate a two\u2010dimensional asynchronous in\u2010phase quadrature histogram. Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. Finally, the accuracy of the method for seven modulation methods is verified by extensive simulations. The experimental results show that under the channel model of additive white Gaussian noise (AWGN), when the SNR of the input signal with low\u2010speed asynchronous sampling is 6\u2009dB, more than 95% of the average recognition accuracy can be achieved, and the effectiveness and robustness of the proposed scheme are verified by comparative experiments.<\/jats:p>","DOI":"10.1049\/2024\/9589239","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T08:44:41Z","timestamp":1722501881000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Asynchronous Wireless Signal Modulation Recognition Based on In\u2010Phase Quadrature Histogram"],"prefix":"10.1049","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9252-5361","authenticated-orcid":false,"given":"Xu","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7411-6122","authenticated-orcid":false,"given":"Xi","family":"Hui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6118-4291","authenticated-orcid":false,"given":"Pengwu","family":"Wan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4317-3122","authenticated-orcid":false,"given":"Tengfei","family":"Hui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3883-000X","authenticated-orcid":false,"given":"Xiongfei","family":"Li","sequence":"additional","affiliation":[]}],"member":"265","published-online":{"date-parts":[[2024,8]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"126","article-title":"Space emitter fine feature identification based on multi-domain fusion","volume":"43","author":"Wang X.","year":"2023","journal-title":"Chinese Space Science and Technology"},{"key":"e_1_2_10_2_2","first-page":"60","article-title":"Research on mathematical modeling method of separation conditions for blind source separation of communication signals","volume":"20","author":"Liu Z.","year":"2023","journal-title":"Space Electronic Technology"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2012.021712.100638"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1049\/sil2.12189"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1049\/sil2.12069"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/26.823550"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/jmse11081632"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/COML.4234"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2932266"},{"key":"e_1_2_10_10_2","first-page":"44","article-title":"Analysis of the statistical model with the two-dimensional cumulant feature applying to modulation classification","volume":"41","author":"Liu P.","year":"2014","journal-title":"Journal of Xidian University"},{"key":"e_1_2_10_11_2","doi-asserted-by":"crossref","unstructured":"QuW. 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