Reduction of Power Envelope Fluctuations in OFDM Signals by using Neural Networks

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 -...

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Bibliographic Details
Published in:IEEE communications letters 2010-07, Vol.14 (7), p.599-601
Main Authors: Jabrane, Younes, Jiménez, Victor P Gil, Armada, Ana Garcia, Said, Brahim Ait Es, Ouahman, Abdellah Ait
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Artificial neural networks
Computer science
control theory
systems
Connectionism. Neural networks
Constellations
Convergence
cubic metric
Envelopes
Exact sciences and technology
Fluctuation
Fluctuations
Gas insulated transmission lines
Information, signal and communications theory
Multi-layer neural network
Multilayer perceptrons
Multiplexing
Neural networks
Neurons
OFDM
Orthogonal Frequency Division Multiplexing
PAPR
Peak to average power ratio
Reduction
Signal and communications theory
Telecommunications and information theory
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2010.07.100385