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Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. In this work, different scaling factors are selected for the generation of [Formula: see text]-QAM frames. The combination of scaling factors, which maximize the classification accuracy is chosen. A convolutional neural network (CNN) with six stages is employed for AMC. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes. <\/jats:p>","DOI":"10.1142\/s0218126623500676","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T07:55:38Z","timestamp":1661759738000},"source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning-Based Robust Automatic Modulation Classification Scheme for Next-Generation Networks"],"prefix":"10.1142","volume":"32","author":[{"given":"Vinoth Babu","family":"Kumaravelu","sequence":"first","affiliation":[{"name":"Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vishnu Vardhan","family":"Gudla","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1038-7096","authenticated-orcid":false,"given":"Arthi","family":"Murugadass","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (AI & ML), Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hindavi","family":"Jadhav","sequence":"additional","affiliation":[{"name":"Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Prakasam","sequence":"additional","affiliation":[{"name":"Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agbotiname Lucky","family":"Imoize","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos, Nigeria"},{"name":"Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"S0218126623500676BIB001","doi-asserted-by":"crossref","first-page":"101259","DOI":"10.1016\/j.phycom.2020.101259","volume":"45","author":"Hou C.","year":"2021","journal-title":"Phys. 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