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Deep Learning Integration in PAPR Reduction in 5G Filter Bank Multicarrier Systems
Mohamed Hussien Moharam , AYA W. Wafik
Abstract:   (113 Views)
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.
Keywords: 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
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Type of Study: Research Paper | Subject: Machine Learning
Received: 2024/09/22 | Revised: 2024/12/15 | Accepted: 2024/12/15
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.