A Novel PAPR Reduction Scheme for OFDM System Based on Deep Learning

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High peak-to-average power ratio (PAPR) has been one of the major drawbacks of orthogonal frequency division multiplexing (OFDM) systems. In this letter, we propose a novel PAPR reduction scheme, known as PAPR reducing network (PRNet), based on the autoencoder architecture of deep learning. In the PRNet, the constellation mapping and demapping of symbols on each subcarrier is determined adaptively through a deep learning technique, such that both the bit error rate (BER) and the PAPR of the OFDM system are jointly minimized. We used simulations to show that the proposed scheme outperforms conventional schemes in terms of BER and PAPR.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-03
Language
English
Article Type
Article
Citation

IEEE COMMUNICATIONS LETTERS, v.22, no.3, pp.510 - 513

ISSN
1089-7798
DOI
10.1109/LCOMM.2017.2787646
URI
http://hdl.handle.net/10203/241206
Appears in Collection
EE-Journal Papers(저널논문)
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