Deep learning based asymmetrical autoencoder for PAPR reduction of CP-OFDM systems

Deep learning based asymmetrical autoencoder for PAPR reduction of CP-OFDM systems

Adoption of Orthogonal Frequency Division Multiplexing (OFDM) in 5th Generation New Radio (5G NR) as a multicarrier modulation technique allows high data rate transmission with lower complexity. However, problem such as peak to average power ratio (PAPR) has decreased the transmitter efficiency. Rec...

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Bibliographic Details
Main Authors: Ezmin Abdullah, Kaharudin Dimyati, Wan Norsyafizan W. Muhamad, Nurain Izzati Shuhaimi, Roslina Mohamad, Nabil M. Hidayat
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:Engineering Science and Technology, an International Journal
Subjects:
Autoencoder
CP-OFDM
Deep learning
OFDM systems
Peak-to-average power ratio
Online Access:http://www.sciencedirect.com/science/article/pii/S2215098623002860

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http://www.sciencedirect.com/science/article/pii/S2215098623002860

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