A Low Complexity PAPR Reduction Scheme for OFDM Systems via Neural Networks

Peak-to-average power ratio (PAPR) reduction is one of the key components in orthogonal frequency division multiplexing (OFDM) systems. Among various PAPR reduction techniques, artificial neural network (NN) has been one of the powerful techniques in reducing the PAPR due to its good generalization...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE communications letters 2014-02, Vol.18 (2), p.225-228
1. Verfasser: Sohn, Insoo
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Peak-to-average power ratio (PAPR) reduction is one of the key components in orthogonal frequency division multiplexing (OFDM) systems. Among various PAPR reduction techniques, artificial neural network (NN) has been one of the powerful techniques in reducing the PAPR due to its good generalization properties with flexible modeling and learning capabilities. In this letter, we propose a new method that uses NNs trained on the active constellation extension (ACE) signals to reduce the PAPR of OFDM signals. Unlike other NN based techniques, the proposed method employs a receiver NN unit, at the OFDM receiver side, achieving significant bit error rate (BER) improvement with low computational complexity.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2013.123113.131888