PAPR Reduction of OFDM Signals using Radial Basis Function Neural

Author

Sohn, InSoo ; Shin, Jaeho

Author_Institution

Dept. of Electron. Eng., Dong-guk Univ., Seoul

fYear

2006

fDate

27-30 Nov. 2006

Firstpage

1

Lastpage

5

Abstract

In this paper we investigate a novel peak-to-average power ratio (PAPR) reduction method based on radial basis function network (RBFN). The RBFN can be regarded as a method of adaptive curve-fitting interpolator and is used to generate optimum mapping pattern to reduce the PAPR in this paper. Our simulation results show that our proposed method has significant performance advantages with low computational complexity compared to the conventional methods.

Keywords

OFDM modulation; computational complexity; curve fitting; interpolation; mobile communication; radial basis function networks; telecommunication computing; OFDM signal; PAPR reduction; RBFN; adaptive curve-fitting interpolator; computational complexity; next generation mobile communication system; optimum mapping pattern generation; radial basis function neural network; Computational modeling; Curve fitting; Interpolation; Mobile communication; Multiuser detection; Neural networks; OFDM modulation; Peak to average power ratio; Power engineering and energy; Radial basis function networks; OFDM; PAPR; RBF;

fLanguage

English

Publisher

ieee

Conference_Titel

Communication Technology, 2006. ICCT '06. International Conference on

Conference_Location

Guilin

Print_ISBN

1-4244-0800-8

Electronic_ISBN

1-4244-0801-6

Type

conf

DOI

10.1109/ICCT.2006.341659

Filename

4146263