{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T02:24:24Z","timestamp":1769221464063,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071024"],"award-info":[{"award-number":["12071024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00034-022-02257-3","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T09:03:21Z","timestamp":1670835801000},"page":"3008-3037","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-label Deep Forest: Towards Automatic Modulation Recognition of Compound Wireless Signals at Low-SNR Environment"],"prefix":"10.1007","volume":"42","author":[{"given":"Liwen","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqi","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9462-580X","authenticated-orcid":false,"given":"Yan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"2257_CR1","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/LSP.2020.2991875","volume":"27","author":"K Bu","year":"2020","unstructured":"K. Bu, Y. He, X. Jing, J. Han, Adversarial transfer learning for deep learning based automatic modulation classification. IEEE Signal Process. Lett. 27, 880\u2013884 (2020)","journal-title":"IEEE Signal Process. Lett."},{"key":"2257_CR2","doi-asserted-by":"crossref","unstructured":"E. Cakir, T. Heittola, H. Huttunen, T. Virtanen, Polyphonic sound event detection using multi label deep neural networks. In 2015 International Joint Conference on Neural Networks (IJCNN) (2015), p. 1\u20137","DOI":"10.1109\/IJCNN.2015.7280624"},{"issue":"8","key":"2257_CR3","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TCOMM.2011.051711.100184","volume":"59","author":"VG Chavali","year":"2011","unstructured":"V.G. Chavali, C.R.C.M. Da Silva, Maximum-likelihood classification of digital amplitude-phase modulated signals in flat fading non-gaussian channels. IEEE Trans. Commun. 59(8), 2051\u20132056 (2011)","journal-title":"IEEE Trans. Commun."},{"issue":"12","key":"2257_CR4","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MCOM.2015.7355585","volume":"53","author":"Z Feng","year":"2015","unstructured":"Z. Feng, C. Qiu, Z. Feng, Z. Wei, W. Li, P. Zhang, An effective approach to 5G: wireless network virtualization. IEEE Commun. Mag. 53(12), 53\u201359 (2015)","journal-title":"IEEE Commun. Mag."},{"issue":"1","key":"2257_CR5","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1109\/SURV.2011.122211.00162","volume":"15","author":"AG Fragkiadakis","year":"2013","unstructured":"A.G. Fragkiadakis, E.Z. Tragos, I.G. Askoxylakis, A survey on security threats and detection techniques in cognitive radio networks. IEEE Commun. Surv. Tutor. 15(1), 428\u2013445 (2013)","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"2257_CR6","doi-asserted-by":"publisher","unstructured":"R.R. Fu, Compound jamming signal recognition based on neural networks. In Sixth International Conference on Instrumentation and Measurement (2016). https:\/\/doi.org\/10.1109\/imccc.2016.163","DOI":"10.1109\/imccc.2016.163"},{"key":"2257_CR7","doi-asserted-by":"publisher","unstructured":"S. Gopal, Y. Yang, Multilabel classification with meta-level features. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, 2010), p. 315\u2013322 https:\/\/doi.org\/10.1145\/1835449.1835503","DOI":"10.1145\/1835449.1835503"},{"issue":"2","key":"2257_CR8","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1109\/TWC.2011.122211.110236","volume":"11","author":"K Hassan","year":"2013","unstructured":"K. Hassan, I. Dayoub, W. Hamouda, C.N. Nzeza, M. Berbineau, Blind digital modulation identification for spatially-correlated MIMO systems. IEEE Trans. Wirel. Commun. 11(2), 683\u2013693 (2013)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"2257_CR9","doi-asserted-by":"publisher","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/cvpr.2016.90"},{"issue":"2","key":"2257_CR10","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/26.380199","volume":"43","author":"CY Huan","year":"1995","unstructured":"C.Y. Huan, A. Polydoros, Likelihood methods for MPSK modulation classification. IEEE Trans. Commun. 43(2), 1493\u20131504 (1995)","journal-title":"IEEE Trans. Commun."},{"key":"2257_CR11","doi-asserted-by":"publisher","first-page":"48827","DOI":"10.1109\/ACCESS.2018.2868224","volume":"6","author":"S Huang","year":"2018","unstructured":"S. Huang, Y. Jiang, X. Qin, Y. Gao, Z. Feng, P. Zhang, Automatic modulation classification of overlapped sources using multi-gene genetic programming with structural risk minimization principle. IEEE Access 6, 48827\u201348839 (2018)","journal-title":"IEEE Access"},{"issue":"7","key":"2257_CR12","doi-asserted-by":"publisher","first-page":"6089","DOI":"10.1109\/TVT.2016.2636324","volume":"66","author":"S Huang","year":"2017","unstructured":"S. Huang, Y. Yao, Z. Wei, Z. Feng, P. Zhang, Automatic modulation classification of overlapped sources using multiple cumulants. IEEE Trans. Veh. Technol. 66(7), 6089\u20136101 (2017)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"21","key":"2257_CR13","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1049\/el.2016.2409","volume":"52","author":"S Huang","year":"2016","unstructured":"S. Huang, Y. Yao, X. Yan, Z. Feng, Cumulant based maximum likelihood classification for overlapped signals. Electron. Lett. 52(21), 1761\u20131763 (2016)","journal-title":"Electron. Lett."},{"key":"2257_CR14","doi-asserted-by":"publisher","unstructured":"W. Jiang, Y. Yi, J. Mao, Z. Huang, X. Wei, CNN-RNN: A unified framework for multi-label image classification. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https:\/\/doi.org\/10.1109\/cvpr.2016.251","DOI":"10.1109\/cvpr.2016.251"},{"issue":"3","key":"2257_CR15","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/j.patcog.2012.09.023","volume":"46","author":"D Kocev","year":"2013","unstructured":"D. Kocev, C. Vens, J. Struyf, S. Dzeroski, Tree ensembles for predicting structured outputs. Pattern Recogn. 46(3), 817\u2013833 (2013)","journal-title":"Pattern Recogn."},{"key":"2257_CR16","unstructured":"A. K. Mccallum, Multi-label text classification with a mixture model trained by EM. In AAAI 99 Workshop on Text Learning (1999)"},{"key":"2257_CR17","doi-asserted-by":"publisher","unstructured":"M.S. M\u00fchlhaus, M. \u00d6ner, O.A. Dobre, H.U. Jkel, F.K. Jondral. Automatic modulation classification for MIMO systems using fourth-order cumulants. In 2012 IEEE Vehicular Technology Conference (VTC Fall) (2012) https:\/\/doi.org\/10.1109\/vtcfall.2012.6399061","DOI":"10.1109\/vtcfall.2012.6399061"},{"key":"2257_CR18","doi-asserted-by":"publisher","unstructured":"T.J. O\u2019Shea, J. Corgan, T.C. Clancy, Convolutional radio modulation recognition networks. In International Conference on Engineering Applications of Neural Networks (2016), p. 213\u2013226. https:\/\/doi.org\/10.1007\/978-3-319-44188-7_16","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"2257_CR19","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1109\/JSTSP.2018.2797022","volume":"12","author":"TJ O\u2019Shea","year":"2017","unstructured":"T.J. O\u2019Shea, T. Roy, T.C. Clancy, Over the air deep learning based radio signal classification. IEEE J. Sel. Top. Signal Process. 12, 168\u2013179 (2017)","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"2257_CR20","doi-asserted-by":"crossref","unstructured":"P. Panagiotou, A. Anastasopoulos, A. Polydoros. Likelihood ratio tests for modulation classification. In 21st Century Military Communications. Architectures and Technologies for Information Superiority (Vol. 2, 2000), p. 670\u2013674","DOI":"10.1109\/MILCOM.2000.904013"},{"issue":"8","key":"2257_CR21","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1109\/26.58753","volume":"38","author":"A Polydoros","year":"1990","unstructured":"A. Polydoros, K. Kim, On the detection and classification of quadrature digital modulations in broad-band noise. IEEE Trans. Commun. 38(8), 1199\u20131211 (1990)","journal-title":"IEEE Trans. Commun."},{"key":"2257_CR22","unstructured":"S. Ramjee, S. Ju, D. Yang, X. Liu, A.E. Gamal, Y.C. Eldar, Fast deep learning for automatic modulation classification (2019). arXiv:1901.05850"},{"key":"2257_CR23","doi-asserted-by":"publisher","unstructured":"T.N. Sainath, O. Vinyals, A. Senior, H. Sak, Convolutional, long short-term memory, fully connected deep neural networks. In ICASSP 2015\u20132015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2015). https:\/\/doi.org\/10.1109\/icassp.2015.7178838","DOI":"10.1109\/icassp.2015.7178838"},{"issue":"2\/3","key":"2257_CR24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"RE Schapire","year":"2000","unstructured":"R.E. Schapire, Y. Singer, Boostexter: A boosting-based system for text categorization. Mach. Learn. 39(2\/3), 135\u2013168 (2000)","journal-title":"Mach. Learn."},{"key":"2257_CR25","doi-asserted-by":"crossref","unstructured":"C.M. Spooner, Classification of co-channel communication signals using cyclic cumulants. In Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers (vol. 1, 1995), p. 531\u2013536","DOI":"10.1109\/ACSSC.1995.540605"},{"issue":"3","key":"2257_CR26","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/26.837045","volume":"48","author":"A Swami","year":"2000","unstructured":"A. Swami, B.M. Sadler, Hierarchical digital modulation classification using cumulants. IEEE Trans. Commun. 48(3), 416\u2013429 (2000)","journal-title":"IEEE Trans. Commun."},{"key":"2257_CR27","doi-asserted-by":"crossref","unstructured":"K. Trohidis, G. Tsoumakas, G. Kalliris, I.P. Vlahavas, Multi-label classification of music into emotions. ISMIR 8, 325\u2013330. Eurasip J. Audio Speech Music Process. 2011(1), 325\u2013330 (2008)","DOI":"10.1186\/1687-4722-2011-426793"},{"issue":"9","key":"2257_CR28","first-page":"1901","volume":"38","author":"Y Wei","year":"2016","unstructured":"Y. Wei, X. Wei, L. Min, J. Huang, B. Ni, D. Jian, Z. Yao, S. Yan, HCP: A flexible CNN framework for multi-label image classification. IEEE Trans. Softw. Eng. 38(9), 1901\u20131907 (2016)","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"2","key":"2257_CR29","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/26.823550","volume":"48","author":"W Wen","year":"2000","unstructured":"W. Wen, J.M. Mendel, Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Trans. Commun. 48(2), 189\u2013193 (2000)","journal-title":"IEEE Trans. Commun."},{"key":"2257_CR30","doi-asserted-by":"publisher","unstructured":"N.E. West, T.J. O\u2019Shea. Deep architectures for modulation recognition. In IEEE International Symposium on Dynamic Spectrum Access Networks (DySAN) (2017). https:\/\/doi.org\/10.1109\/dyspan.2017.7920754","DOI":"10.1109\/dyspan.2017.7920754"},{"key":"2257_CR31","unstructured":"L. Yang, X. Wu, Y. Jiang, Z. Zhou. Multi-label deep forest. In ECAI 2020\u201424th European Conference on Artificial Intelligence, Santiago de Compostela, Spain, Volume 325 of Frontiers in Artificial Intelligence and Applications (2020), p. 1634\u20131641"},{"issue":"3","key":"2257_CR32","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.dt.2021.02.005","volume":"18","author":"H Yu","year":"2022","unstructured":"H. Yu, X. Yan, S. Liu, P. Li, X. Hao, Radar emitter multi-label recognition based on residual network. Def. Technol. 18(3), 410\u2013417 (2022)","journal-title":"Def. Technol."},{"issue":"9","key":"2257_CR33","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1049\/iet-spr.2011.0357","volume":"6","author":"M Zaerin","year":"2012","unstructured":"M. Zaerin, B. Seyfe, Multiuser modulation classification based on cumulants in additive white gaussian noise channel. IET Signal Process. 6(9), 815\u2013823 (2012)","journal-title":"IET Signal Process."},{"key":"2257_CR34","doi-asserted-by":"publisher","unstructured":"M. Zhang, K. Zhang, Multi-label learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010) (2010), p. 999\u20131008. https:\/\/doi.org\/10.1145\/1835804.1835930","DOI":"10.1145\/1835804.1835930"},{"issue":"8","key":"2257_CR35","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M Zhang","year":"2014","unstructured":"M. Zhang, Z. Zhou, A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819\u20131837 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2257_CR36","doi-asserted-by":"crossref","unstructured":"Z. Zhou, J. Feng. Deep forest: towards an alternative to deep neural networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI\u201917 (AAAI Press, Melbourne, Australia, 2017), p. 3553\u20133559","DOI":"10.24963\/ijcai.2017\/497"},{"key":"2257_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2019.107393","volume":"169","author":"M Zhu","year":"2020","unstructured":"M. Zhu, Y. Li, Z. Pan, J. Yang, Automatic modulation recognition of compound signals using a deep multi-label classifier: a case study with radar jamming signals. Signal Process. 169, 107393 (2020)","journal-title":"Signal Process."}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-022-02257-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-022-02257-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-022-02257-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T06:16:06Z","timestamp":1728540966000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-022-02257-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":37,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["2257"],"URL":"https:\/\/doi.org\/10.1007\/s00034-022-02257-3","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]},"assertion":[{"value":"13 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}