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Although the existing AMR methods have been mature for the orthogonal signals, these methods face challenges when deployed in non-orthogonal transmission systems due to the superimposed signals. In this paper, we aim to develop efficient AMR methods for both downlink and uplink non-orthogonal transmission signals using deep learning-based data-driven classification methodology. Specifically, for downlink non-orthogonal signals, we propose a Bi-directional Long Short-Term Memory (BiLSTM)-based AMR method that exploits long-term data dependence to automatically learn irregular signal constellation shapes. Transfer learning is further incorporated to improve recognition accuracy and robustness under varying transmission conditions. For uplink non-orthogonal signals, the combinatorial number of classification types explodes exponentially with the number of signal layers, which becomes the major obstacle to AMR. We develop a spatio-temporal fusion network based on the attention mechanism to efficiently extract spatio-temporal features, and network details are optimized according to the superposition characteristics of non-orthogonal signals. Experiments show that the proposed deep learning-based methods outperform their conventional counterparts in both downlink and uplink non-orthogonal systems. In a typical uplink scenario with three non-orthogonal signal layers, the recognition accuracy can approach 96.6% in the Gaussian channel, which is 19% higher than the vanilla Convolution Neural Network.<\/jats:p>","DOI":"10.3390\/s23115234","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T02:39:47Z","timestamp":1685587187000},"page":"5234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning-Aided Modulation Recognition for Non-Orthogonal Signals"],"prefix":"10.3390","volume":"23","author":[{"given":"Jiaqi","family":"Fan","sequence":"first","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Linna","family":"Wu","sequence":"additional","affiliation":[{"name":"Aerospace System Engineering Shanghai, Shanghai 201108, China"}]},{"given":"Jinbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Science and Technology on Communication Networks Laboratory, The 54th Research Institute of CETC, Shijiazhuang 050081, China"}]},{"given":"Junwei","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Zhong","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Laboratory of Electromagnetic Space Cognition and Intelligent Control, Beijing 100083, China"}]},{"given":"Zehui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1109\/TCCN.2022.3169740","article-title":"NAS-AMR: Neural Architecture Search-Based Automatic Modulation Recognition for Integrated Sensing and Communication Systems","volume":"8","author":"Zhang","year":"2022","journal-title":"IEEE Trans. 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