{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:50:57Z","timestamp":1761745857199,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,14]]},"DOI":"10.1145\/3614008.3614013","type":"proceedings-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T18:19:52Z","timestamp":1697566792000},"page":"26-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Interference signal recognition processing based on convolutional neural network"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6404-0247","authenticated-orcid":false,"given":"Yi Ze","family":"Li","sequence":"first","affiliation":[{"name":"The School of Electronic Information, Xijing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0349-2341","authenticated-orcid":false,"given":"Ji","family":"Xiang","sequence":"additional","affiliation":[{"name":"The School of Electronic Information, Xijing University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"volume-title":"On the Energy Detection of Unknown Signals Over Fading Channels","author":"Digham F.F","key":"e_1_3_2_1_1_1","unstructured":"Digham F.F , Alouini M,S . Simon M. K. 2007 , On the Energy Detection of Unknown Signals Over Fading Channels .IEEE Transactions on Communications . Digham F.F,Alouini M,S.Simon M. K.2007, On the Energy Detection of Unknown Signals Over Fading Channels.IEEE Transactions on Communications."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Yang M.C LI Y Liu X F etal2015.Cyclostationary Feature Detection Based Spectrum Sensing Algorithm Under Complicated Electromagnetic Environment in Cognitive Radio Networks.China Communications.  Yang M.C LI Y Liu X F et al.2015.Cyclostationary Feature Detection Based Spectrum Sensing Algorithm Under Complicated Electromagnetic Environment in Cognitive Radio Networks.China Communications.","DOI":"10.1109\/CC.2015.7275257"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Zhang X.Z Gao F.F Chai R etal 2015.Matched Filter BasedSpectrum Sensing When Primary User Has Multiple PowerLevels.China Communications.  Zhang X.Z Gao F.F Chai R et al. 2015.Matched Filter BasedSpectrum Sensing When Primary User Has Multiple PowerLevels.China Communications.","DOI":"10.1109\/CC.2015.7084399"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Liu C Wang J Liu X.M 2019.Maximum Eigenvaluebased Goodness-of-fit Detection for Spectrum Sensing in Cognitive Radio.IEEE Transactions on Vehicular Technology.  Liu C Wang J Liu X.M 2019.Maximum Eigenvaluebased Goodness-of-fit Detection for Spectrum Sensing in Cognitive Radio.IEEE Transactions on Vehicular Technology.","DOI":"10.1109\/TVT.2019.2923648"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Zheng S.L Chen S.C Qi P.H etal 2020.Spectrum SensingBased on Deep Learning Classification for Cognitive Radios.China Communications.  Zheng S.L Chen S.C Qi P.H et al. 2020.Spectrum SensingBased on Deep Learning Classification for Cognitive Radios.China Communications.","DOI":"10.23919\/JCC.2020.02.012"},{"key":"e_1_3_2_1_6_1","unstructured":"Peter A.O Anastasios D Sangarapillai L. 2018.SpatioTemporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-aided SVM Algorithms. IEEE Access.  Peter A.O Anastasios D Sangarapillai L. 2018.SpatioTemporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-aided SVM Algorithms. IEEE Access."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"BAO J NIE C LIU B 2019.Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs.IEEE Access.  BAO J NIE C LIU B 2019.Improved Blind Spectrum Sensing by Covariance Matrix Cholesky Decomposition and RBF-SVM Decision Classification at Low SNRs.IEEE Access.","DOI":"10.1109\/ACCESS.2019.2929316"},{"volume-title":"2019.Deep Learning for Spectrum Sensing","author":"Gao J.B","key":"e_1_3_2_1_8_1","unstructured":"Gao J.B , Yi X.M , Zhong C , 2019.Deep Learning for Spectrum Sensing . IEEE Wireless Communications Letters . Gao J.B,Yi X.M,Zhong C,et al.2019.Deep Learning for Spectrum Sensing.IEEE Wireless Communications Letters."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Liu R Ma Y ZHANG X 2021.Deep Learning-based Spectrum Sensing in Space-Air-Ground Integrated Networks.Journal of Communications and InformationNetworks.  Liu R Ma Y ZHANG X 2021.Deep Learning-based Spectrum Sensing in Space-Air-Ground Integrated Networks.Journal of Communications and InformationNetworks.","DOI":"10.23919\/JCIN.2021.9387707"},{"volume-title":"A CNN-based Deep Learning Approach","author":"Xie J.D","key":"e_1_3_2_1_10_1","unstructured":"Xie J.D , Liu C , Liang Y.C , Activity Pattern Aware Spectrum Sensing : A CNN-based Deep Learning Approach . IEEE Communications Letters . Xie J.D,Liu C,Liang Y.C,et al.2019.Activity Pattern Aware Spectrum Sensing: A CNN-based Deep Learning Approach.IEEE Communications Letters."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Xie J.D Fang J Liu C etal 2020.Deep Learning-based Spectrum Sensing in Cognitive Radio:A CNN-LSTM Approach.IEEE Communications Letters.  Xie J.D Fang J Liu C et al. 2020.Deep Learning-based Spectrum Sensing in Cognitive Radio:A CNN-LSTM Approach.IEEE Communications Letters.","DOI":"10.1109\/LCOMM.2020.3002073"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Ni T Ding X.J Wang Y F etal 2021.Spectrum Sensing via Temporal Convolutional Network.China Communications .  Ni T Ding X.J Wang Y F et al. 2021.Spectrum Sensing via Temporal Convolutional Network.China Communications .","DOI":"10.23919\/JCC.2021.09.004"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Liu X Y Yang D.Y GAMAL A.E. 2017.Deep Neural Network Architectures for Modulation Classification \u2225 2017 51st Asilomar Conference on Signals Systems and Computers. Pacific Grove:IEEE.  Liu X Y Yang D.Y GAMAL A.E. 2017.Deep Neural Network Architectures for Modulation Classification \u2225 2017 51st Asilomar Conference on Signals Systems and Computers. Pacific Grove:IEEE.","DOI":"10.1109\/ACSSC.2017.8335483"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"O\u2019 SHEA T.J Roy T Clancy T.C. 2018.Over-the-air Deep Learning Based Radio Signal Classification. IEEE Journal of Selected Topics in Signal Processing.  O\u2019 SHEA T.J Roy T Clancy T.C. 2018.Over-the-air Deep Learning Based Radio Signal Classification. IEEE Journal of Selected Topics in Signal Processing.","DOI":"10.1109\/JSTSP.2018.2797022"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Wang Y Liu M Yang J 2019.Data-driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios.IEEE Transactions on Vehicular Technology.  Wang Y Liu M Yang J 2019.Data-driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios.IEEE Transactions on Vehicular Technology.","DOI":"10.1109\/TVT.2019.2900460"},{"key":"e_1_3_2_1_16_1","volume":"202","author":"Zhang H.Z","unstructured":"Zhang H.Z , Huang M , Yang J.J. 202 1.A Data Preprocessing Method for Automatic Modulation Classification Based on CNN.IEEE Communications Letters. Zhang H.Z,Huang M,Yang J.J. 2021.A Data Preprocessing Method for Automatic Modulation Classification Based on CNN.IEEE Communications Letters.","journal-title":"Yang J.J."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Liao K Zhao Y Gu J 2021.Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification. IEEE Access.  Liao K Zhao Y Gu J 2021.Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification. IEEE Access.","DOI":"10.1109\/ACCESS.2021.3053427"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"A. Hirose. 2011.Nature of complex number and complex-valued neural networks. Frontiers of Electrical and Electronic Engineering in China.  A. Hirose. 2011.Nature of complex number and complex-valued neural networks. Frontiers of Electrical and Electronic Engineering in China.","DOI":"10.1007\/s11460-011-0125-3"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"K. He X. Zhang S. Ren 2015.Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.  K. He X. Zhang S. Ren 2015.Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification.","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_1_20_1","unstructured":"M. A. Nielsen.2015.Neural Networks and Deep Learning. Determination Press.  M. A. Nielsen.2015.Neural Networks and Deep Learning. Determination Press."},{"key":"e_1_3_2_1_21_1","volume":"201","author":"Kingma","unstructured":"D. P. Kingma , J. Ba. 201 4. Adam: A Method for Stochastic Optimization. Computer Science. D. P. Kingma, J. Ba. 2014. Adam: A Method for Stochastic Optimization. Computer Science.","journal-title":"J. Ba."},{"key":"e_1_3_2_1_22_1","unstructured":"S. Ruder. 2016.An overview of gradient descent optimization algorithms. ArXiv.  S. Ruder. 2016.An overview of gradient descent optimization algorithms. ArXiv."},{"key":"e_1_3_2_1_23_1","unstructured":"C. Trabelsi O. Bilaniuk Y. Zhang 2017.Deep Complex Networks. ArXiv.  C. Trabelsi O. Bilaniuk Y. Zhang 2017.Deep Complex Networks. ArXiv."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"D. P. Mandic and V. S. L. Goh. 2009.Complex Valued Nonlinear Adaptive Filters. Wiley-Blackwell.  D. P. Mandic and V. S. L. Goh. 2009.Complex Valued Nonlinear Adaptive Filters. Wiley-Blackwell.","DOI":"10.1002\/9780470742624"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"K. He X. Zhang S. Ren 2015.Deep Residual Learning for Image Recognition. ArXiv.  K. He X. Zhang S. Ren 2015.Deep Residual Learning for Image Recognition. ArXiv.","DOI":"10.1109\/CVPR.2016.90"}],"event":{"name":"SPML 2023: 2023 6th International Conference on Signal Processing and Machine Learning","acronym":"SPML 2023","location":"Tianjin China"},"container-title":["2023 6th International Conference on Signal Processing and Machine Learning (SPML)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3614008.3614013","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3614008.3614013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:27Z","timestamp":1750178247000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3614008.3614013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":25,"alternative-id":["10.1145\/3614008.3614013","10.1145\/3614008"],"URL":"https:\/\/doi.org\/10.1145\/3614008.3614013","relation":{},"subject":[],"published":{"date-parts":[[2023,7,14]]},"assertion":[{"value":"2023-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}