DocumentCode :
54362
Title :
A Low Complexity PAPR Reduction Scheme for OFDM Systems via Neural Networks
Author :
Insoo Sohn
Author_Institution :
Div. of Electron. & Electr. Eng., Dongguk Univ. - Seoul, Seoul, South Korea
Volume :
18
Issue :
2
fYear :
2014
fDate :
Feb-14
Firstpage :
225
Lastpage :
228
Abstract :
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.
Keywords :
OFDM modulation; error statistics; neural nets; radio receivers; telecommunication computing; ACE signal; BER improvement; OFDM system; active constellation extension signal; artificial neural network; bit error rate; low complexity PAPR reduction scheme; orthogonal frequency division multiplexing systems; peak-to-average power ratio reduction; receiver NN unit; Artificial neural networks; Bit error rate; Frequency-domain analysis; Peak to average power ratio; Time-domain analysis; Training; ACE; OFDM; PAPR; neural networks;
fLanguage :
English
Journal_Title :
Communications Letters, IEEE
Publisher :
ieee
ISSN :
1089-7798
Type :
jour
DOI :
10.1109/LCOMM.2013.123113.131888
Filename :
6708129
Link To Document :
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