{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:40:19Z","timestamp":1784608819486,"version":"3.55.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T00:00:00Z","timestamp":1697673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4401904"],"award-info":[{"award-number":["2022YFB4401904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19B2015"],"award-info":[{"award-number":["U19B2015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["2021ZDLGY0209"],"award-info":[{"award-number":["2021ZDLGY0209"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project on Artificial Intelligence of Xi\u2019an Science and Technology Plan","award":["2022JH-RGZN-0003"],"award-info":[{"award-number":["2022JH-RGZN-0003"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.<\/jats:p>","DOI":"10.1186\/s13634-023-01061-8","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T15:03:12Z","timestamp":1697727792000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An intelligent signal processing method against impulsive noise interference in AIoT"],"prefix":"10.1186","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2940-3001","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"issue":"01","key":"1061_CR1","first-page":"61","volume":"47","author":"G Wang","year":"2022","unstructured":"G. Wang, H. Ren, G. Zhao et al., Digital model and giant system coupling technology system of a smart coal mine. J. China Coal Soc. 47(01), 61\u201374 (2022)","journal-title":"J. China Coal Soc."},{"issue":"8","key":"1061_CR2","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1109\/LCOMM.2021.3081593","volume":"25","author":"X Yang","year":"2021","unstructured":"X. Yang, X. Yu, C. Zhang et al., MineGPS: battery-free localization base station for coal mine environment. IEEE Commun. Lett. 25(8), 2579\u20132583 (2021)","journal-title":"IEEE Commun. Lett."},{"issue":"1","key":"1061_CR3","first-page":"378","volume":"10","author":"D Wu","year":"2023","unstructured":"D. Wu, M. Sun, P. Zhang et al., Personalized secure demand-oriented data service toward edge-cloud collaborative IoT. IEEE IoT J. 10(1), 378\u2013390 (2023)","journal-title":"IEEE IoT J."},{"key":"1061_CR4","doi-asserted-by":"crossref","unstructured":"D. Wu, S. Wu, D.B. Rawat, et al., Special issue on knowledge and service-oriented industrial internet of things: architectures, challenges, and methodologies, IEEE IoT J. 9(18), pp. 16738\u201316741 (2022)","DOI":"10.1109\/JIOT.2022.3187543"},{"issue":"6","key":"1061_CR5","first-page":"9266","volume":"6","author":"D Wu","year":"2019","unstructured":"D. Wu, Z. Zhang, S. Wu et al., Biologically inspired resource allocation for network slices in 5G-enabled internet of things. IEEE IoT J. 6(6), 9266\u20139279 (2019)","journal-title":"IEEE IoT J."},{"issue":"2","key":"1061_CR6","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/TR.2021.3062045","volume":"70","author":"M Wang","year":"2021","unstructured":"M. Wang, Y. Lin, Q. Tian et al., Transfer learning promotes 6G wireless communications: recent advances and future challenges. IEEE Trans. Rel. 70(2), 790\u2013807 (2021)","journal-title":"IEEE Trans. Rel."},{"issue":"5","key":"1061_CR7","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/MWC.001.1900493","volume":"27","author":"X Liu","year":"2020","unstructured":"X. Liu, Q. Sun, W. Lu, C. Wu, H. Ding, Big-data-based intelligent spectrum sensing for heterogeneous spectrum communications in 5G. IEEE Wirel. Commun. 27(5), 67\u201373 (2020)","journal-title":"IEEE Wirel. Commun."},{"key":"1061_CR8","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1016\/j.dcan.2022.10.013","volume":"8","author":"C Chen","year":"2022","unstructured":"C. Chen, G. Yao, C. Wang et al., Enhancing the robustness of object detection via 6G vehicular edge computing. Digit. Commun. Netw. 8, 923\u2013931 (2022)","journal-title":"Digit. Commun. Netw."},{"issue":"9","key":"1061_CR9","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/JSAC.2020.3000884","volume":"38","author":"Y Liu","year":"2020","unstructured":"Y. Liu, S. Zhang, F. Gao et al., Uplink-aided high mobility downlink channel estimation over massive MIMO-OTFS system. IEEE J. Sel. Areas Commun. 38(9), 1994\u20132009 (2020)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"10","key":"1061_CR10","doi-asserted-by":"publisher","first-page":"6832","DOI":"10.1109\/TCOMM.2022.3199019","volume":"70","author":"M Li","year":"2022","unstructured":"M. Li, S. Zhang, Y. Ge et al., Joint channel estimation and data detection for hybrid RIS aided millimeter wave OTFS systems. IEEE Trans. Commun. 70(10), 6832\u20136848 (2022)","journal-title":"IEEE Trans. Commun."},{"issue":"1","key":"1061_CR11","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/TCCN.2020.3024610","volume":"7","author":"Y Lin","year":"2021","unstructured":"Y. Lin, Y. Tu, Z. Dou, L. Chen, S. Mao, Contour stella image and deep learning for signal recognition in the physical layer. IEEE Trans. Cogn. Commun. Netw. 7(1), 34\u201346 (2021)","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"issue":"6","key":"1061_CR12","doi-asserted-by":"publisher","first-page":"3387","DOI":"10.1109\/TCOMM.2023.3263566","volume":"71","author":"L Chen","year":"2023","unstructured":"L. Chen, L. Fan, X. Lei, T.Q. Duong, A. Nallanathan, G.K. Karagiannidis, Relay-assisted federated edge learning: performance analysis and system optimization. IEEE Trans. Commun. 71(6), 3387\u20133401 (2023)","journal-title":"IEEE Trans. Commun."},{"issue":"2","key":"1061_CR13","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1109\/TR.2022.3148114","volume":"71","author":"B Wang","year":"2022","unstructured":"B. Wang, K. Xu, S. Zheng et al., A deep learning-based intelligent signal processing method for improving the reliability of the MIMO wireless communication system. IEEE Trans. Rel. 71(2), 1104\u20131115 (2022)","journal-title":"IEEE Trans. Rel."},{"issue":"1","key":"1061_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TCCN.2020.3018736","volume":"7","author":"S Zheng","year":"2021","unstructured":"S. Zheng, S. Chen, X. Yang, DeepReceiver: a deep learning-based intelligent signal processing method for wireless communications in the physical layer. IEEE Trans. Cogn. Commun. Netw. 7(1), 5\u201320 (2021)","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"issue":"4","key":"1061_CR15","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1109\/MNET.001.2100590","volume":"36","author":"X Liu","year":"2022","unstructured":"X. Liu, Z. Li, B. Wang et al., Transform-domain-based cognitive radio networks for harsh interference environments. IEEE Netw. 36(4), 78\u201385 (2022)","journal-title":"IEEE Netw."},{"key":"1061_CR16","unstructured":"J.D. Ni, Soft-decision-data reshuffle to mitigate impulsive radio frequency interference impact on low-density-parity-check code performance, in: AIAA Annual Technology Symposium 2011 Houston (2011)"},{"key":"1061_CR17","doi-asserted-by":"crossref","unstructured":"S.V. Zhidkov, On the analysis of OFDM receiver with blanking nonlinearity in impulsive noise channels, in: Proceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004., 2004, pp. 492\u2013496 (2005)","DOI":"10.1109\/ISPACS.2004.1439104"},{"issue":"7","key":"1061_CR18","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1109\/JSAC.2013.130702","volume":"31","author":"J Lin","year":"2013","unstructured":"J. Lin, M. Nassar, B.L. Evans, Impulsive noise mitigation in powerline communications using sparse Bayesian learning. IEEE J. Sel. Areas Commun. 31(7), 1172\u20131183 (2013)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"3","key":"1061_CR19","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TPWRD.2013.2258474","volume":"28","author":"N Andreadou","year":"2013","unstructured":"N. Andreadou, M. Tonelloa, On the mitigation of impulsive noise in power-line communications with LT codes. IEEE Trans. Power Deliv. 28(3), 1483\u20131490 (2013)","journal-title":"IEEE Trans. Power Deliv."},{"key":"1061_CR20","doi-asserted-by":"crossref","unstructured":"T. Shongwe, A.J.H. Vinck, H.C. Ferreira, On impulsive noise and its models, 18th IEEE International Symposium on Power Line Communications and Its Applications (Glasgow, UK, 2014), pp. 12\u201317","DOI":"10.1109\/ISPLC.2014.6812360"},{"issue":"3","key":"1061_CR21","doi-asserted-by":"publisher","first-page":"119","DOI":"10.23919\/SAIEE.2015.8531938","volume":"106","author":"T Shongwe","year":"2015","unstructured":"T. Shongwe, A.J.H. Vinck, H.C. Ferreira, A study on impulsive noise and its models. SAIEE Afr. Res. J. 106(3), 119\u2013131 (2015)","journal-title":"SAIEE Afr. Res. J."},{"key":"1061_CR22","doi-asserted-by":"crossref","unstructured":"T. Shongwe, A.J. Han Vinck, H.C. Ferreira, The effects of periodic impulsive noise on OFDM, 2015 IEEE International Symposium on Power Line Communications and Its Applications (ISPLC), Austin, TX, USA, pp. 189\u2013194 (2015)","DOI":"10.1109\/ISPLC.2015.7147612"},{"key":"1061_CR23","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1109\/LSP.2021.3108908","volume":"28","author":"C Chen","year":"2021","unstructured":"C. Chen, W. Xu, Y. Pan, H. Zhu, J. Wang, Rank correlation based detection of known signals in middleton\u2019s class-A noise. IEEE Signal Process. Lett. 28, 1988\u20131992 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"1061_CR24","doi-asserted-by":"crossref","unstructured":"M.D. Kubjana, A.R. Ndjiongue, T. Shongwe, Impulsive noise evaluation on PLC-VLC based on DCO-OFDM, in: 2018 11th International Symposium on Communication Systems, Networks Digital Signal Processing (CSNDSP), Budapest, Hungary, pp. 1-6 (2018)","DOI":"10.1109\/CSNDSP.2018.8471851"},{"issue":"8","key":"1061_CR25","doi-asserted-by":"publisher","first-page":"5115","DOI":"10.1109\/TIT.2017.2676104","volume":"63","author":"ML de Freitas","year":"2017","unstructured":"M.L. de Freitas, M. Egan, L. Clavier, A. Goupil, G.W. Peters, N. Azzaoui, Capacity bounds for additive symmetric \u03b1-stable noise channels. IEEE Trans. Inf. Theory 63(8), 5115\u20135123 (2017)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"4","key":"1061_CR26","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1109\/TPWRD.2015.2390134","volume":"30","author":"G Laguna-Sanchez","year":"2015","unstructured":"G. Laguna-Sanchez, M. Lopez-Guerrero, On the use of alpha-stable distributions in noise modeling for PLC. IEEE Trans. Power Deliv. 30(4), 1863\u20131870 (2015)","journal-title":"IEEE Trans. Power Deliv."},{"issue":"3","key":"1061_CR27","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1109\/TSP.2018.2887400","volume":"67","author":"G Tzagkarakis","year":"2019","unstructured":"G. Tzagkarakis, J.P. Nolan, P. Tsakalides, Compressive sensing using symmetric alpha-stable distributions for robust sparse signal reconstruction. IEEE Trans. Signal Process. 67(3), 808\u2013820 (2019)","journal-title":"IEEE Trans. Signal Process."},{"key":"1061_CR28","doi-asserted-by":"crossref","unstructured":"J. Gao, W. Zhang, Y. Liu, H. Wang, J. Zhao, High-performance concatenation decoding of reed solomon codes with SPC codes, IEEE Trans. Very Large Scale Integr. (VLSI) Syst, 29(9), pp. 1670\u20131674 (2021)","DOI":"10.1109\/TVLSI.2021.3097155"},{"issue":"4","key":"1061_CR29","doi-asserted-by":"publisher","first-page":"956","DOI":"10.23919\/JSEE.2021.000082","volume":"32","author":"W Hui","year":"2021","unstructured":"W. Hui, Q. Xiaohui, L. Jie, Stability analysis of linear\/nonlinear switching active disturbance rejection control based MIMO continuous systems. J. Syst. Eng. Electron. 32(4), 956\u2013970 (2021)","journal-title":"J. Syst. Eng. Electron."},{"issue":"3","key":"1061_CR30","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1109\/JSTSP.2022.3140660","volume":"16","author":"Y Guo","year":"2022","unstructured":"Y. Guo, R. Zhao, S. Lai, L. Fan, X. Lei, G.K. Karagiannidis, Distributed machine learning for multiuser mobile edge computing systems. IEEE J. Sel. Top. Signal Process. 16(3), 460\u2013473 (2022)","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"1061_CR31","doi-asserted-by":"crossref","unstructured":"H. Huang, G. Song, G, Guan, et al., Deep learning for physical-layer 5G wireless techniques: opportunities, challenges and solutions, IEEE Wirel. Commun., 27(1), 214\u2013222 (2020)","DOI":"10.1109\/MWC.2019.1900027"},{"issue":"1","key":"1061_CR32","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/TNSE.2022.3207214","volume":"10","author":"Y Zhang","year":"2023","unstructured":"Y. Zhang, C. Chen, L. Liu et al., Aerial edge computing on orbit: a task offloading and allocation scheme. IEEE Trans. Netw. Sci. Eng 10(1), 275\u2013285 (2023)","journal-title":"IEEE Trans. Netw. Sci. Eng"},{"issue":"5","key":"1061_CR33","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.1109\/TII.2020.2987421","volume":"17","author":"X Liu","year":"2021","unstructured":"X. Liu, C. Sun, M. Zhou, C. Wu, B. Peng, P. Li, Reinforcement learning-based multislot double-threshold spectrum sensing with bayesian fusion for industrial big spectrum data. IEEE Trans. Ind. Inform. 17(5), 3391\u20133400 (2021)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"1","key":"1061_CR34","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/TNSE.2022.3207214","volume":"10","author":"Y Zhang","year":"2023","unstructured":"Y. Zhang, C. Chen, L. Liu, D. Lan, H. Jiang, S. Wan, Aerial edge computing on orbit: a task offloading and allocation scheme. IEEE Trans. Netw. Sci. Eng. 10(1), 275\u2013285 (2023)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"11","key":"1061_CR35","doi-asserted-by":"publisher","first-page":"4500","DOI":"10.1109\/TNNLS.2019.2955777","volume":"31","author":"SR Dubey","year":"2020","unstructured":"S.R. Dubey, S. Chakraborty, S.K. Roy, S. Mukherjee, S.K. Singh, B.B. Chaudhuri, diffGrad: an optimization method for convolutional neural networks. IEEE Trans. Neural Netw. Learn. Syst 31(11), 4500\u20134511 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-023-01061-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-023-01061-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-023-01061-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T05:13:58Z","timestamp":1730351638000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-023-01061-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,19]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["1061"],"URL":"https:\/\/doi.org\/10.1186\/s13634-023-01061-8","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,19]]},"assertion":[{"value":"27 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"104"}}