Applications of feedforward neural networks to WCDMA power amplifier model

Author

Songbai, He ; Xiaohuan, Yan ; Jingfu, Bao

Author_Institution

Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

Volume

5

fYear

2005

fDate

4-7 Dec. 2005

Abstract

In this paper, we propose a feedforward neural network model (FFNN) of the class B power amplifier (PA) for wideband code division multiple access (WCDMA) communication systems. The amplifier operation frequency is 1920-1980MHz, and 1dB compression output power is 30dBm. According to the measured data, we get the AM/AM and AM/PM neural network model which uses back propagation (BP) training algorithm. The simulation result shows that the characteristic of the NN model matches that of the power amplifier well. The model is very effective for characterizing the power amplifiers. One application of the power amplifiers model is linear compensation techniques such as predistortion methods, which are used to improve the severe nonlinearity of AM/AM and AM/PM characteristics.

Keywords

UHF power amplifiers; backpropagation; code division multiple access; compensation; feedforward neural nets; 1.92 to 1.98 GHz; AM/AM characteristic; AM/PM characteristic; WCDMA communication systems; back propagation; feedforward neural networks; linear compensation; power amplifier model; predistortion methods; Broadband amplifiers; Feedforward neural networks; Frequency; Multiaccess communication; Neural networks; Operational amplifiers; Power amplifiers; Power generation; Power system modeling; Predistortion;

fLanguage

English

Publisher

ieee

Conference_Titel

Microwave Conference Proceedings, 2005. APMC 2005. Asia-Pacific Conference Proceedings

Print_ISBN

0-7803-9433-X

Type

conf

DOI

10.1109/APMC.2005.1606979

Filename

1606979