Deep Learning-Based PAPR Suppression of OFDM Signals with Clipping Constraint

被引：0

作者：

Ohta, Masaya ^{[1
]}

Hayakawa, Toko ^{[1
]}

机构：

[1] Osaka Metropolitan Univ, Grad Sch Informat, 1-1 Gakuen Cho,Naka Ku, Sakai, Osaka 5998531, Japan

来源：

2024 INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS, AND COMMUNICATIONS, ITC-CSCC 2024 | 2024年

关键词：

OFDM; Deep Learning; PRNet; PAPR; Clipping constraint;

D O I：

10.1109/ITC-CSCC62988.2024.10628189

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study proposes a method to improve suppression performance by introducing a clipping-constraint into the loss function of PRNet, a deep learning model suitable for PAPR suppression in OFDM signals. Numerical experiments have confirmed that while maintaining BER performance equivalent to that of normal OFDM signals, this method improves PAPR performance by approximately 1.5 dB compared to the conventional method and about 4.5 dB compared to normal OFDM signals.

引用

页数：4

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