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
收藏
页码:339 / 342
页数:4
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