A review on deep learning aided pilot decontamination in massive MIMO
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 ar...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2322822 |
| _version_ | 1797290394871922688 |
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| author | Crallet M. Victor Alloys N. Mvuma Salehe I. Mrutu |
| author_facet | Crallet M. Victor Alloys N. Mvuma Salehe I. Mrutu |
| author_sort | Crallet M. Victor |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-07T19:21:45Z |
| format | Article |
| id | doaj.art-413c7a222e164f0d8ef940e3887847a3 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-07T19:21:45Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-413c7a222e164f0d8ef940e3887847a32024-02-29T09:37:38ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2322822A review on deep learning aided pilot decontamination in massive MIMOCrallet M. Victor0Alloys N. Mvuma1Salehe I. Mrutu2College of Informatics and Virtual Education, University of Dodoma, Dodoma, TanzaniaCollege of Information and Communication Technology, Mbeya University of Science and Technology, Mbeya, TanzaniaCollege of Informatics and Virtual Education, University of Dodoma, Dodoma, TanzaniaAbstractIn 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.https://www.tandfonline.com/doi/10.1080/23311916.2024.2322822Pilot contaminationchannel estimationdeep learningdeep neural networkspilot assignment and designmassive MIMO |
| spellingShingle | Crallet M. Victor Alloys N. Mvuma Salehe I. Mrutu A review on deep learning aided pilot decontamination in massive MIMO Cogent Engineering Pilot contamination channel estimation deep learning deep neural networks pilot assignment and design massive MIMO |
| title | A review on deep learning aided pilot decontamination in massive MIMO |
| title_full | A review on deep learning aided pilot decontamination in massive MIMO |
| title_fullStr | A review on deep learning aided pilot decontamination in massive MIMO |
| title_full_unstemmed | A review on deep learning aided pilot decontamination in massive MIMO |
| title_short | A review on deep learning aided pilot decontamination in massive MIMO |
| title_sort | review on deep learning aided pilot decontamination in massive mimo |
| topic | Pilot contamination channel estimation deep learning deep neural networks pilot assignment and design massive MIMO |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2322822 |
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