{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:45:09Z","timestamp":1776437109376,"version":"3.51.2"},"reference-count":64,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Automatic modulation recognition (AMR) is used in various domains\u2014from general-purpose communication to many military applications\u2014thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.<\/jats:p>","DOI":"10.3390\/s22197488","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"7488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5167-4553","authenticated-orcid":false,"given":"Asad","family":"Hussain","sequence":"first","affiliation":[{"name":"Faculty of Engineering & Computer Sciences, National University of Modern Languages, Islamabad 44000, Pakistan"},{"name":"Department of Engineering and Applied Sciences, University of Bergamo, 24129 Bergamo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3498-475X","authenticated-orcid":false,"given":"Sheraz","family":"Alam","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & Computer Sciences, National University of Modern Languages, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3687-6093","authenticated-orcid":false,"given":"Sajjad A.","family":"Ghauri","sequence":"additional","affiliation":[{"name":"School of Engineering & Applied Sciences, ISRA University, Islamabad Campus, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2467-4045","authenticated-orcid":false,"given":"Mubashir","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Management, Information and Production Engineering, University of Bergamo, 24129 Bergamo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8152-4065","authenticated-orcid":false,"given":"Husnain Raza","family":"Sherazi","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of West London, London W5 5RF, UK"}]},{"given":"Adnan","family":"Akhunzada","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, University of Doha for Science and Technology, Doha 24449, Qatar"}]},{"given":"Iram","family":"Bibi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Comsats University, Islamabad 45550, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4388-020X","authenticated-orcid":false,"given":"Abdullah","family":"Gani","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Informatics, University Malaysia Sabah, Kota Kinabalu 88400, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Khan, W.U., Ihsan, A., Li, X., Li, J., and Tsiftsis, T.A. (2022). Backscatter sensors communication for 6G low-powered NOMA-enabled IoT networks under imperfect SIC. arXiv.","DOI":"10.1109\/JSYST.2022.3194705"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13294","DOI":"10.1109\/TVT.2021.3121146","article-title":"Optimal resource allocation and task segmentation in iot enabled mobile edge cloud","volume":"70","author":"Mahmood","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khan, W.U., Ihsan, A., Nguyen, T.N., Javed, M.A., and Ali, Z. (2022). NOMA-enabled Backscatter Communications for Green Transportation in Automotive-Industry 5.0. IEEE Trans. Ind. Inform., 1.","DOI":"10.1109\/TII.2022.3161029"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.comcom.2022.04.017","article-title":"Weighted utility aware computational overhead minimization of wireless power mobile edge cloud","volume":"190","author":"Mahmood","year":"2022","journal-title":"Comput. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"36757","DOI":"10.1109\/ACCESS.2020.2974809","article-title":"Partial offloading in energy harvested mobile edge computing: A direct search approach","volume":"8","author":"Mahmood","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Usman, M., and Lee, J.A. (2020, January 21\u201323). AMC-IoT: Automatic modulation classification using efficient convolutional neural networks for low powered IoT devices. Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC49870.2020.9289261"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.1007\/s12083-021-01176-5","article-title":"Energy efficiency maximization for beyond 5G NOMA-enabled heterogeneous networks","volume":"14","author":"Khan","year":"2021","journal-title":"Peer-Peer Netw. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1109\/LWC.2021.3105728","article-title":"An enhanced spectrum reservation framework for heterogeneous users in CR-enabled IoT networks","volume":"10","author":"Tanveer","year":"2021","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khan, W.U., Jamshed, M.A., Lagunas, E., Chatzinotas, S., Li, X., and Ottersten, B. (2022). Energy Efficiency Optimization for Backscatter Enhanced NOMA Cooperative V2X Communications Under Imperfect CSI. IEEE Trans. Intell. Transp. Syst., 1\u201312.","DOI":"10.1109\/TITS.2022.3187567"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101296","DOI":"10.1016\/j.phycom.2021.101296","article-title":"Optimal power allocation for NOMA-enabled D2D communication with imperfect SIC decoding","volume":"46","author":"Yu","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_11","unstructured":"Ali, Z., Lagunas, E., Mahmood, A., Asif, M., Ihsan, A., Chatzinotas, S., Ottersten, B., and Dobre, O.A. (2022). Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3547","DOI":"10.1109\/TITS.2020.3001682","article-title":"Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications","volume":"22","author":"Jameel","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khan, W.U., Lagunas, E., Ali, Z., Javed, M.A., Ahmed, M., Chatzinotas, S., Ottersten, B., and Popovski, P. (2022). Opportunities for physical layer security in UAV communication enhanced with intelligent reflective surfaces. arXiv.","DOI":"10.1109\/MWC.001.2200125"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ihsan, A., Chen, W., Asif, M., Khan, W.U., and Li, J. (2022). Energy-efficient IRS-aided NOMA beamforming for 6G wireless communications. arXiv.","DOI":"10.1109\/TGCN.2022.3209617"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e4458","DOI":"10.1002\/ett.4458","article-title":"Federated learning and next generation wireless communications: A survey on bidirectional relationship","volume":"33","author":"Shome","year":"2022","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khan, W.U., Ali, Z., Lagunas, E., Chatzinotas, S., and Ottersten, B. (2022). Rate Splitting Multiple Access for Cognitive Radio GEO-LEO Co-Existing Satellite Networks. arXiv.","DOI":"10.1109\/GLOBECOM48099.2022.10000999"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khan, W.U., Lagunas, E., Mahmood, A., Chatzinotas, S., and Ottersten, B. (2022). When RIS meets geo satellite communications: A new optimization framework in 6G. arXiv.","DOI":"10.1109\/VTC2022-Spring54318.2022.9860805"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hasan, T., Malik, J., Bibi, I., Khan, W.U., Al-Wesabi, F.N., Dev, K., and Huang, G. (2022). Securing industrial internet of things against botnet attacks using hybrid deep learning approach. IEEE Trans. Netw. Sci. Eng.","DOI":"10.36227\/techrxiv.19313318"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"134695","DOI":"10.1109\/ACCESS.2020.3009849","article-title":"Hybrid deep learning: An efficient reconnaissance and surveillance detection mechanism in SDN","volume":"8","author":"Malik","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1109\/LCOMM.2017.2717821","article-title":"{k}-Sparse autoencoder-based automatic modulation classification with low complexity","volume":"21","author":"Ali","year":"2017","journal-title":"IEEE Commun. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15859","DOI":"10.1109\/JSEN.2020.3012046","article-title":"Security-aware data-driven intelligent transportation systems","volume":"21","author":"Malik","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_22","unstructured":"Krayani, A., Alam, A.S., Calipari, M., Marcenaro, L., Nallanathan, A., and Regazzoni, C. (July, January 14). Automatic Modulation Classification in Cognitive-IoT Radios using Generalized Dynamic Bayesian Networks. Proceedings of the 7th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9419764","DOI":"10.1155\/2022\/9419764","article-title":"Random Graph-Based M-QAM Classification for MIMO Systems","volume":"2022","author":"Sarfraz","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_24","first-page":"575","article-title":"Mathematical Modelling of Engineering Problems","volume":"8","author":"Muhammad","year":"2021","journal-title":"IIETA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"19733","DOI":"10.1109\/ACCESS.2017.2746140","article-title":"Robust automatic modulation classification under varying noise conditions","volume":"5","author":"Wu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Moldovanu, S., Damian Michis, F.A., Biswas, K.C., Culea-Florescu, A., and Moraru, L. (2021). Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques. Cancers, 13.","DOI":"10.3390\/cancers13215256"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.engappai.2010.08.008","article-title":"Blind digital modulation classification in software radio using the optimized classifier and feature subset selection","volume":"24","author":"Ebrahimzadeh","year":"2011","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bibi, I., Akhunzada, A., Malik, J., Ahmed, G., and Raza, M. (2019, January 21\u201322). An effective Android ransomware detection through multi-factor feature filtration and recurrent neural network. Proceedings of the UK\/China Emerging Technologies (UCET), Glasgow, UK.","DOI":"10.1109\/UCET.2019.8881884"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"129600","DOI":"10.1109\/ACCESS.2020.3009819","article-title":"A dynamic DL-driven architecture to combat sophisticated Android malware","volume":"8","author":"Bibi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3428151","article-title":"Secure Distributed Mobile Volunteer Computing with Android","volume":"22","author":"Bibi","year":"2021","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jiang, K., Qin, X., Zhang, J., and Wang, A. (2021). Modulation Recognition of Communication Signal Based on Convolutional Neural Network. Symmetry, 13.","DOI":"10.3390\/sym13122302"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ge, Z., Jiang, H., Guo, Y., and Zhou, J. (2021). Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network. Sensors, 21.","DOI":"10.3390\/s21248252"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, K., Gao, W., and Huang, Q. (2021). Automatic modulation recognition based on a DCN-BiLSTM network. Sensors, 21.","DOI":"10.3390\/s21051577"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7723","DOI":"10.1007\/s00521-020-05514-1","article-title":"Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification","volume":"33","author":"Zheng","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"117689","DOI":"10.1109\/ACCESS.2020.2981130","article-title":"Deep learning for robust automatic modulation recognition method for IoT applications","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"114931","DOI":"10.1016\/j.eswa.2021.114931","article-title":"Automatic modulation classification using different neural network and PCA combinations","volume":"178","author":"Ali","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jiang, Y., Wang, B., Zhang, L., and Chen, W. (2020, January 11\u201313). Automatic Modulation Classification based on Wiener filter preprocessing and Cumulants. Proceedings of the IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC49862.2020.9338946"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhou, X., Xiong, J., Li, F., and Wang, L. (2020, January 21\u201323). Automatic modulation recognition based on multi-dimensional feature extraction. Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China.","DOI":"10.1109\/WCSP49889.2020.9299797"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"154290","DOI":"10.1109\/ACCESS.2020.3017641","article-title":"Automatic modulation classification scheme based on LSTM with random erasing and attention mechanism","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"16362","DOI":"10.1109\/ACCESS.2020.2966019","article-title":"Automatic modulation classification based on novel feature extraction algorithms","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s11277-021-08236-2","article-title":"Modulation classification of MFSK modulated signals using spectral centroid","volume":"119","author":"Baris","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1007\/s00034-021-01854-y","article-title":"CSA-Assisted Gabor Features for Automatic Modulation Classification","volume":"41","author":"Shah","year":"2021","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.neucom.2021.05.010","article-title":"Deep cascading network architecture for robust automatic modulation classification","volume":"455","author":"Weng","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1109\/LSP.2017.2752459","article-title":"Automatic modulation classification using deep learning based on sparse autoencoders with nonnegativity constraints","volume":"24","author":"Ali","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6089","DOI":"10.1109\/TVT.2016.2636324","article-title":"Automatic modulation classification of overlapped sources using multiple cumulants","volume":"66","author":"Huang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nie, Y., Shen, X., Huang, S., Zhang, Y., and Feng, Z. (2017, January 19\u201322). Automatic modulation classification based multiple cumulants and quasi-newton method for mimo system. Proceedings of the Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA.","DOI":"10.1109\/WCNC.2017.7925863"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Triantafyllakis, K., Surligas, M., Vardakis, G., and Papadakis, S. (2017, January 6\u20139). Phasma: An automatic modulation classification system based on random forest. Proceedings of the International Symposium on Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, USA.","DOI":"10.1109\/DySPAN.2017.7920749"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Mihandoost, S., and Amirani, M.C. (2016, January 27\u201328). Automatic modulation classification using combination of wavelet transform and GARCH model. Proceedings of the 8th International Symposium on Telecommunications (IST), Tehran, Iran.","DOI":"10.1109\/ISTEL.2016.7881868"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TWC.2016.2623716","article-title":"Low complexity automatic modulation classification based on order-statistics","volume":"16","author":"Han","year":"2016","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dai, A., Zhang, H., and Sun, H. (2016, January 6\u201310). Automatic modulation classification using stacked sparse auto-encoders. Proceedings of the 13th International Conference on Signal Processing (ICSP), Chengdu, China.","DOI":"10.1109\/ICSP.2016.7877834"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kim, S.J., and Yoon, D. (2016, January 19\u201321). Automatic modulation classification in practical wireless channels. Proceedings of the International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC.2016.7763329"},{"key":"ref_52","unstructured":"Zhao, Z., Wang, S., Zhang, W., and Xie, Y. (2016, January 5\u20138). A novel automatic modulation classification method based on Stockwell-transform and energy entropy for underwater acoustic signals. Proceedings of the International Conference on Signal Processing, Communications and Computing (ICSPCC), Hong Kong, China."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Lu, H., Jia, L., and Mao, K. (2016, January 24\u201329). Automatic modulation classification with genetic backpropagation neural network. Proceedings of the Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7744380"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"71","DOI":"10.31436\/iiumej.v17i2.641","article-title":"KNN based classification of digital modulated signals","volume":"17","author":"Ghauri","year":"2016","journal-title":"IIUM Eng. J."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xu, H., Przystupa, K., Fang, C., Marciniak, A., Kochan, O., and Beshley, M. (2020). A combination strategy of feature selection based on an integrated optimization algorithm and weighted k-nearest neighbor to improve the performance of network intrusion detection. Electronics, 9.","DOI":"10.3390\/electronics9081206"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Abdelmutalab, A., Assaleh, K., and El-Tarhuni, M. (2014, January 1\u20132). Automatic modulation classification using polynomial classifiers. Proceedings of the 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC), Washington, DC, USA.","DOI":"10.1109\/PIMRC.2014.7136275"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Satija, U., Mohanty, M., and Ramkumar, B. (2015, January 19\u201320). Automatic modulation classification using S-transform based features. Proceedings of the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2015.7095322"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/CJECE.2016.2570250","article-title":"Automatic modulation classification based on kernel density estimation","volume":"39","author":"Abuella","year":"2016","journal-title":"Can. J. Electr. Comput. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1049\/iet-com.2015.1124","article-title":"On classifiers for blind feature-based automatic modulation classification over multiple-input\u2013multiple-output channels","volume":"10","author":"Kharbech","year":"2016","journal-title":"IET Commun."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s11277-014-2183-3","article-title":"Automatic modulation recognition in wireless multi-carrier wireless systems with cepstral features","volume":"81","author":"Keshk","year":"2015","journal-title":"Wirel. Pers. Commun."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.engappai.2006.08.004","article-title":"Classification of modulation signals using statistical signal characterization and artificial neural networks","volume":"20","author":"Hossen","year":"2007","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.engappai.2009.05.006","article-title":"Using fuzzy clustering and TTSAS algorithm for modulation classification based on constellation diagram","volume":"23","author":"Ahmadi","year":"2010","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e4762","DOI":"10.1002\/dac.4762","article-title":"A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends","volume":"34","year":"2021","journal-title":"Int. J. Commun. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4199","DOI":"10.1007\/s11277-017-4378-x","article-title":"A novel approach for automatic modulation classification via hidden Markov models and Gabor features","volume":"96","author":"Ghauri","year":"2017","journal-title":"Wirel. Pers. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7488\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:45:39Z","timestamp":1760143539000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":64,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197488"],"URL":"https:\/\/doi.org\/10.3390\/s22197488","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]}}}