Reduction of power envelope fluctuations in OFDM signals by using neural networks

Authors:
Younes Jabrane;Víctor P. Gil Jiménez;Ana García Armada;Brahim Ait Es Said;Abdellah Ait Ouahman
Affiliations:
Team of Telecommunications and Informatics Networks, University Cadi Ayyad;Department of Signal Theory and Communications, University Carlos III de Madrid;Department of Signal Theory and Communications, University Carlos III de Madrid;Team of Telecommunications and Informatics Networks, University Cadi Ayyad;Team of Telecommunications and Informatics Networks, University Cadi Ayyad
Venue:
IEEE Communications Letters
Year:
2010

Citing 2
Cited 0

Applications of neural networks to digital communications: a survey

Signal Processing - Special issue on emerging techniques for communication terminals
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the main drawbacks of Orthogonal Frequency Division Multiplexing (OFDM) are the large fluctuations of its power envelope. In this letter, a novel and efficient scheme based on Multilayer Perceptron (MLP) Neural Networks (NN) is proposed. The NN synthesizes the Active Constellation Expansion - (ACE) technique which is able to drastically reduce envelope fluctuations. This is achieved with much lower complexity, faster convergence, and better performance compared to previously available methods.