{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:23:00Z","timestamp":1775665380850,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,30]],"date-time":"2017-08-30T00:00:00Z","timestamp":1504051200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, we propose a deep neural network (DNN)-based automatic modulation classification (AMC) for digital communications. While conventional AMC techniques perform well for additive white Gaussian noise (AWGN) channels, classification accuracy degrades for fading channels where the amplitude and phase of channel gain change in time. The key contributions of this paper are in two phases. First, we analyze the effectiveness of a variety of statistical features for AMC task in fading channels. We reveal that the features that are shown to be effective for fading channels are different from those known to be good for AWGN channels. Second, we introduce a new enhanced AMC technique based on DNN method. We use the extensive and diverse set of statistical features found in our study for the DNN-based classifier. The fully connected feedforward network with four hidden layers are trained to classify the modulation class for several fading scenarios. Numerical evaluation shows that the proposed technique offers significant performance gain over the existing AMC methods in fading channels.<\/jats:p>","DOI":"10.3390\/e19090454","type":"journal-article","created":{"date-parts":[[2017,8,30]],"date-time":"2017-08-30T10:40:00Z","timestamp":1504089600000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Jung","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaekyum","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byeoungdo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongweon","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3733-0148","authenticated-orcid":false,"given":"Jun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Biomedical Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhu, Z., and Nandi, A.K. (2014). Automatic Modulation Classification: Principles, Algorithms and Applications, John Wiley & Sons.","DOI":"10.1002\/9781118906507"},{"key":"ref_2","unstructured":"Dobre, O.A., Abdi, A., Bar-Ness, Y., and Su, W. (2005, January 18\u201319). Blind modulation classification: A concept whose time has come. Proceedings of the IEEE\/Sarnoff Symposium on Advances in Wired and Wireless Communication, Princeton, NJ, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hazza, A.M., Alshebeili, S., and Fahad, A. (2013, January 12\u201314). An overview of feature-based methods for digital modulation classification. Proceedings of the 1st International Conference on Communications, Signal Processing, and Their Applications, Sharjah, UAE.","DOI":"10.1109\/ICCSPA.2013.6487244"},{"key":"ref_4","unstructured":"Azzouz, E., and Nandi, A.K. (2013). 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