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dc.contributor.authorLi, Jun
dc.contributor.authorDang, Shuping
dc.contributor.authorWen, Miaowen
dc.contributor.authorZhang, Zhenrong
dc.contributor.authorLi, Qiang
dc.date.accessioned2021-03-02T12:08:21Z
dc.date.available2021-03-02T12:08:21Z
dc.date.issued2021-03-01
dc.identifier.citationLi, J., Dang, S., Wen, M., Zhang, Z., & Li, Q. (2021). Smart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation. IEEE Wireless Communications Letters, 1–1. doi:10.1109/lwc.2021.3062686
dc.identifier.issn2162-2345
dc.identifier.doi10.1109/LWC.2021.3062686
dc.identifier.urihttp://hdl.handle.net/10754/667806
dc.description.abstractSince the proposal of orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM), the high detection complexity of this novel modulation scheme becomes a major issue degrading its applicability in practice. To provide an easy-to-implement and low-complexity solution to the detection of the OFDM-SNM signal, we design a cascaded artificial neural network (CANN) at the OFDM-SNM receiver to decouple the detection of subcarrier number pattern (SNP) and constellation symbols on the active subcarriers. This is the first time that the CANN design is introduced to assist in detecting parallel signals. Numerical simulations verify the effectiveness and efficiency of the proposed smart detection scheme and show that the CANN based smart detection with adequate training can yield comparable error performance to the optimal maximum-likelihood detection at much lower complexity.
dc.description.sponsorshipThis work was supported in part by National Natural Science Foundation of China under Grants 61872102 and 61661004, in part by the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2020A0505100061, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, in part by the Fundamental Research Funds for the Central Universities under Grant 2019SJ02, and in part by the Guangxi Science Key Research and Development Project under Grant AB1850043.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9365714/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9365714
dc.rightsArchived with thanks to IEEE Wireless Communications Letters
dc.subjectArtificial neural network (ANN)
dc.subjectorthogonal frequency-division multiplexing (OFDM)
dc.subjectsubcarrier number modulation
dc.subjectlow-complexity detection
dc.subjectcascaded architecture.
dc.titleSmart Detection Using the Cascaded Artificial Neural Network for OFDM with Subcarrier Number Modulation
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalIEEE Wireless Communications Letters
dc.eprint.versionPost-print
dc.contributor.institutionResearch Center of Intelligent Communication Engineering, School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China.
dc.contributor.institutionSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
dc.contributor.institutionSchool of Computer, Electronics and Information, Guangxi University, and Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, China.
dc.contributor.institutionCollege of Information Science and Technology, Jinan University, Guangzhou 510632, China.
kaust.personDang, Shuping
refterms.dateFOA2021-03-02T13:09:08Z
dc.date.published-online2021-03-01
dc.date.published-print2021-06


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