PRNet with convolution layer for PAPR reduction of OFDM signals

被引：0

作者：

Ohta, Masaya ^{[1
]}

Kubota, Koichi ^{[1
]}

机构：

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

来源：

IEICE COMMUNICATIONS EXPRESS | 2024年 / 13卷 / 08期

关键词：

OFDM; deep learning; autoencoder; PAPR; PRNet;

D O I：

10.23919/comex.2024XBL0091

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 research uses deep learning to address the high peak-toaverage power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPRreducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.

引用

页码：339 / 342

页数：4

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