{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:47:47Z","timestamp":1774374467985,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20484"],"award-info":[{"award-number":["U21A20484"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LQ22F020030"],"award-info":[{"award-number":["LQ22F020030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022C01016"],"award-info":[{"award-number":["2022C01016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["U21A20484"],"award-info":[{"award-number":["U21A20484"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ22F020030"],"award-info":[{"award-number":["LQ22F020030"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2022C01016"],"award-info":[{"award-number":["2022C01016"]}]},{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"publisher","award":["U21A20484"],"award-info":[{"award-number":["U21A20484"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ22F020030"],"award-info":[{"award-number":["LQ22F020030"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017599","name":"Science and Technology Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2022C01016"],"award-info":[{"award-number":["2022C01016"]}],"id":[{"id":"10.13039\/501100017599","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).<\/jats:p>","DOI":"10.3390\/s23020951","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"951","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7332-1169","authenticated-orcid":false,"given":"Danfeng","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanlong","family":"Xi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8293-6897","authenticated-orcid":false,"given":"Abdullah","family":"Yaqot","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Applied Science L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7619-8015","authenticated-orcid":false,"given":"Horst","family":"Hellbr\u00fcck","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Applied Science L\u00fcbeck, 23562 L\u00fcbeck, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huifeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1109\/JIOT.2019.2943696","article-title":"Dynamic edge access system in IoT environment","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1109\/TII.2021.3077865","article-title":"A Data Stream Cleaning System Using Edge Intelligence for Smart City Industrial Environments","volume":"18","author":"Sun","year":"2021","journal-title":"IEEE Trans. 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