Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems
High peak-to-average power ratio (PAPR) has been a major drawback of Filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It c...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Iran University of Science and Technology
2024-11-01
|
| Series: | Iranian Journal of Electrical and Electronic Engineering |
| Subjects: | |
| Online Access: | http://ijeee.iust.ac.ir/article-1-3459-en.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841550976877592576 |
|---|---|
| author | Mohamed Hussien Moharam AYA W. wafik |
| author_facet | Mohamed Hussien Moharam AYA W. wafik |
| author_sort | Mohamed Hussien Moharam |
| collection | DOAJ |
| description | High peak-to-average power ratio (PAPR) has been a major drawback of Filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It combines tone reservation with sliding window (SW-TR). It also combines them with active constellation extension (TRACE) and with deep learning (TR-Net). TR-net decreases the greatest PAPR reduction by around 8.6 dB compared to the original value. This work significantly advances PAPR reduction in FBMC systems by proposing three hybrid methods, emphasizing the deep learning-based TRNet technique as a groundbreaking solution for efficient, distortion-free signal processing. |
| format | Article |
| id | doaj-art-2829a6d3a22146a6a1f58cea8d956364 |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-2829a6d3a22146a6a1f58cea8d9563642025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-01204115125Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier SystemsMohamed Hussien Moharam0AYA W. wafik1 Assistant Professor at Misr University for Science and Technology, Electronics and Communications Engineering Department, Giza, Egypt Cyber Security Engineer graduate from Misr University for Science and Technology, Electronics and Communications Engineering Department, Giza, Egypt. High peak-to-average power ratio (PAPR) has been a major drawback of Filter bank Multicarrier (FBMC) in the 5G system. This research aims to calculate the PAPR reduction associated with the FBMC system. This research uses four techniques to reduce PAPR. They are classical tone reservation (TR). It combines tone reservation with sliding window (SW-TR). It also combines them with active constellation extension (TRACE) and with deep learning (TR-Net). TR-net decreases the greatest PAPR reduction by around 8.6 dB compared to the original value. This work significantly advances PAPR reduction in FBMC systems by proposing three hybrid methods, emphasizing the deep learning-based TRNet technique as a groundbreaking solution for efficient, distortion-free signal processing.http://ijeee.iust.ac.ir/article-1-3459-en.pdffilter bank multi-carrier (fbmc)peak-to-average power ratio (papr)tone reservation (tr)sliding window tone reservation (sw-tr)offset quadrature amplitude modulation (oqam)trace detection (trace)tone reservation neural network (trnet)deep lea |
| spellingShingle | Mohamed Hussien Moharam AYA W. wafik Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems Iranian Journal of Electrical and Electronic Engineering filter bank multi-carrier (fbmc) peak-to-average power ratio (papr) tone reservation (tr) sliding window tone reservation (sw-tr) offset quadrature amplitude modulation (oqam) trace detection (trace) tone reservation neural network (trnet) deep lea |
| title | Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems |
| title_full | Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems |
| title_fullStr | Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems |
| title_full_unstemmed | Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems |
| title_short | Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems |
| title_sort | deep learning integration in papr reduction in 5g filter bank multicarrier systems |
| topic | filter bank multi-carrier (fbmc) peak-to-average power ratio (papr) tone reservation (tr) sliding window tone reservation (sw-tr) offset quadrature amplitude modulation (oqam) trace detection (trace) tone reservation neural network (trnet) deep lea |
| url | http://ijeee.iust.ac.ir/article-1-3459-en.pdf |
| work_keys_str_mv | AT mohamedhussienmoharam deeplearningintegrationinpaprreductionin5gfilterbankmulticarriersystems AT ayawwafik deeplearningintegrationinpaprreductionin5gfilterbankmulticarriersystems |