Neural Network Based Simplified Clipping and Filtering Technique for PAPR Reduction of OFDM Signals

被引：64

作者：

Sohn, Insoo ^{[1
]}

Kim, Sung Chul ^{[2
]}

机构：

[1] Dongguk Univ, Div Elect & Elect Engn, Seoul 100715, South Korea

[2] Myongji Univ, Dept Informat & Commun Engn, Yongin 449728, South Korea

来源：

IEEE COMMUNICATIONS LETTERS | 2015年 / 19卷 / 08期

关键词：

OFDM; PAPR; cubic metric; clipping and filtering; neural networks; POWER REDUCTION; TRANSMISSION; SYSTEMS;

D O I：

10.1109/LCOMM.2015.2441065

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Many iterative clipping and filtering (ICF) based techniques have been proposed that achieve similar peak-to-average power ratio (PAPR) reduction of orthogonal frequency division multiplexing (OFDM) signals as the original ICF, but with lower complexity, such as the simplified clipping and filtering (SCF) technique. However, these low complexity methods require numerous complex fast Fourier transform (FFT) operations and parameter calculations. In this letter, we introduce a novel ICF method that uses an optimized mapper based on artificial neural network and SCF techniques. Compared to the conventional ICF based methods, the proposed scheme offers desirable cubic metric (CM) and bit error rate (BER) simulation results with significantly reduced computational complexity.

引用

页码：1438 / 1441

页数：4