A review on deep learning aided pilot decontamination in massive MIMO

Simple item page
dc.contributor.authorVictor, Crallet M.
dc.contributor.authorMvuma, Alloys N.
dc.contributor.authorMrutu, Salehe I.
dc.date.accessioned2024-08-16T09:10:40Z
dc.date.available2024-08-16T09:10:40Z
dc.date.issued2024
dc.descriptionFull text. 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.other10.1080/23311916.2024.2322822
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4586
dc.language.isoen
dc.publisherInforma UK Limited
dc.relation.ispartofCogent Engineering
dc.titleA review on deep learning aided pilot decontamination in massive MIMO
dc.typejournal-article
oaire.citation.issue1
oaire.citation.volume11
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A_review_on_deep_learning_aided_pilot_decontaminat.pdf
Size:
1.62 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Journal Articles