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dc.contributor.authorCui, Wenqi
dc.contributor.authorDong, Anming
dc.contributor.authorCao, Yi
dc.contributor.authorZhang, Chuanting
dc.contributor.authorYu, Jiguo
dc.contributor.authorLi, Sufang
dc.date.accessioned2021-08-31T12:05:24Z
dc.date.available2021-08-31T12:05:24Z
dc.date.issued2021-06-12
dc.identifier.citationCui, W., Dong, A., Cao, Y., Zhang, C., Yu, J., & Li, S. (2021). Deep Learning Based MIMO Transmission with Precoding and Radio Transformer Networks. Procedia Computer Science, 187, 396–401. doi:10.1016/j.procs.2021.04.078
dc.identifier.issn1877-0509
dc.identifier.doi10.1016/j.procs.2021.04.078
dc.identifier.urihttp://hdl.handle.net/10754/670864
dc.description.abstractIn this paper, we study MIMO transmission schemes based on deep learning (DL). We propose a novel DL-based MIMO communication structure by combing a beamforming network at the transmitter side and a radio transformer network (RTN) at the receiver side. Compared with the classical DL-based MIMO communication systems, the interference is potentially mitigated by a precoding network and a RTN network, which is thus beneficial to improve the performance of signal detection. Simulation results show that the proposed scheme outperforms the classical MIMO transmission schemes in terms of bit error rate (BER).
dc.description.sponsorshipThis work was supported in part by the National Key R&D Program of China under grant 2019YFB2102600, the National Natural Science Foundation of China (NSFC) under Grants 61701269, 61832012, 61771289 and 61672321, the Shandong Provincial Natural Science Foundation under Grant ZR2017BF012, the Key Research and Development Program of Shandong Province under Grants 2019JZZY010313 and 2019JZZY020124, the Joint Research Fund for Young Scholars in Qilu University (Shandong Academy of Sciences) under Grant 2017BSHZ005.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S1877050921008747
dc.rightsThis is an open access article under the CC BY-NC-ND license.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDeep Learning
dc.subjectMultiple-Input Multiple Output (MIMO)
dc.subjectPrecoding
dc.subjectRadio Transformer Network
dc.titleDeep Learning Based MIMO Transmission with Precoding and Radio Transformer Networks
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.conference.date2020-11-27 to 2020-11-29
dc.conference.name9th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2020
dc.conference.locationZhuhai, CHN
dc.identifier.wosutWOS:000681301200063
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353 China
dc.contributor.institutionShandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014 China
dc.contributor.institutionSchool of Information Science and Engineering, University of Jinan, Jinan 250022 China
dc.identifier.volume187
dc.identifier.pages396-401
kaust.personZhang, Chuanting
dc.identifier.eid2-s2.0-85112534289
refterms.dateFOA2021-08-31T12:06:17Z
dc.date.published-online2021-06-12
dc.date.published-print2021


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