Improving compensation of nonlinear distortions in OFDM system using recurrent neural network with conjugate gradient algorithm

Author

Pochmara, Janusz

Author_Institution

Inst. of Electron. & Telecommun., Poznan Univ. of Technol., Poland

Volume

1

fYear

2004

fDate

5-8 Sept. 2004

Firstpage

180

Abstract

The paper presents a neural network predistortion technique compensating for nonlinear distortions caused by an HPA (high power amplifier) cascaded with a filter in an OFDM (orthogonal frequency division multiplexing) system. It is confirmed by computer simulation that the proposed approach produces a faster convergence speed than the conventional backpropagation algorithm. The predistortion technique based on a neural network is very attractive from the implementation point of view, because of the low amount of RAM required and rapid convergence from a blind start.

Keywords

OFDM modulation; backpropagation; conjugate gradient methods; convergence of numerical methods; feedforward neural nets; nonlinear distortion; power amplifiers; radio transmitters; recurrent neural nets; OFDM system; RAM; backpropagation algorithm; conjugate gradient algorithm; convergence; feedforward neural network; filter; high power amplifier; nonlinear distortion compensation; orthogonal frequency division multiplexing; predistortion technique; recurrent neural network; transmitter; Backpropagation algorithms; Computer simulation; Convergence; Filters; High power amplifiers; Neural networks; Nonlinear distortion; OFDM; Predistortion; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Personal, Indoor and Mobile Radio Communications, 2004. PIMRC 2004. 15th IEEE International Symposium on

Print_ISBN

0-7803-8523-3

Type

conf

DOI

10.1109/PIMRC.2004.1370860

Filename

1370860