A review on deep learning aided pilot decontamination in massive MIMO

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dc.contributor.authorVictor, Crallet M.
dc.contributor.authorMvuma, Alloys N.
dc.contributor.authorMrutu, Salehe I.
dc.date.accessioned2024-08-16T13:14:25Z
dc.date.available2024-08-16T13:14:25Z
dc.date.issued2024-02-29
dc.descriptionFull- text article. Also available at https://doi.org/10.1080/23311916.2024.2322822
dc.description.abstractIn multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC.
dc.identifier.citationVictor, C. M., Mvuma, A. N., & Mrutu, S. I. (2024). A review on deep learning aided pilot decontamination in massive MIMO. Cogent Engineering, 11(1), 2322822.
dc.identifier.doi10.1080/23311916.2024.2322822
dc.identifier.otherDOI: https://doi.org/10.1080/23311916.2024.2322822
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4631
dc.language.isoen
dc.publisherInforma UK Limited
dc.relation.ispartofCogent Engineering
dc.subjectPilot contamination
dc.subjectChannel estimation
dc.subjectDeep learning
dc.subjectDeep neural networks
dc.subjectMassive MIMO
dc.subjectMassive multiple-input
dc.subjectMultiple-output
dc.subjectmultiple-input
dc.subjectMIMO
dc.titleA review on deep learning aided pilot decontamination in massive MIMO
dc.typeArticle
oaire.citation.issue1
oaire.citation.volume11
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