Abdullah, Ezmin and Dimyati, Kaharudin and Muhamad, Wan Norsyafizan W. and Shuhaimi, Nurain Izzati and Mohamad, Roslina and Hidayat, Nabil M. (2024) Deep learning based asymmetrical autoencoder for PAPR reduction of CP-OFDM systems. Enegineering Science and Technology-An International Journal-JESTECH, 50. p. 101608. ISSN 2215-0986, DOI https://doi.org/10.1016/j.jestch.2023.101608.
Full text not available from this repository.Abstract
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. Recently, several attempts have been made to reduce the high PAPR in OFDM utilizing deep learning (DL) based on autoencoder architecture. However, the proposed autoencoder using symmetrical autoencoder (SAE) is followed by high computational complexity at both transmitter and receiver as well as BER performance degradation. Since 5G NR focuses on massive 5G internet of things eco-system, a flexible carrier spacing is used to support diverse spectrum bands which is afterward named as Cyclic Prefix OFDM (CP-OFDM). In this study, we aimed to contribute to this growing area of research by exploring the potential of our proposed asymmetrical autoencoder (AAE) to reduce high PAPR in the CP-OFDM system. Four AAE models have been developed in this study and the performance of the models were evaluated based on comprehensive conditions such as data training at different corruption levels, cyclic prefix length, upsampling factors and loss function levels. The investigation of AAE in 5G CP-OFDM system has shown superior performance using a 5x1 AAE model that can reduce a substantial amount of PAPR, BER degradation and computational complexity compared to conventional SAE. This study lays the groundwork for future research into the asymmetrical approach of autoencoder especially in 5G and beyond networks.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Teknologi MARA |
| Uncontrolled Keywords: | Autoencoder; CP-OFDM; Deep learning; OFDM systems; Peak -to -average power ratio |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Nov 2024 02:36 |
| Last Modified: | 12 Nov 2024 02:36 |
| URI: | http://eprints.um.edu.my/id/eprint/45780 |
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