PAPR Reduction of GFDM Signals Using Encoder-Decoder Neural Network (Autoencoder)

These days, one of the major downsides of Generalized Frequency Division Multiplexing (GFDM) systems is a high peak-to-average power ratio (PAPR). In this research, we present a novel deep learning autoencoder-based method to lower the PAPR of GFDM. The PAPR-reducing network (PRNet), also known as t...

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Vydáno v:National Academy science letters Ročník 46; číslo 3; s. 213 - 217
Hlavní autoři: Chakravarty, Sumit, Kumar, Arun
Médium: Journal Article
Jazyk:angličtina
Vydáno: New Delhi Springer India 01.06.2023
Témata:
History of Science
Humanities and Social Sciences
multidisciplinary
Science
Science (multidisciplinary)
Short Communication
Deep learning
Peak-to-average power ratio
Autoencoder
Generalized frequency division multiplexing
ISSN:0250-541X, 2250-1754
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Popis
Shrnutí:These days, one of the major downsides of Generalized Frequency Division Multiplexing (GFDM) systems is a high peak-to-average power ratio (PAPR). In this research, we present a novel deep learning autoencoder-based method to lower the PAPR of GFDM. The PAPR-reducing network (PRNet), also known as the PAPR-reducing method, is based on the encoder-decoder neural network (Autoencoder). In the PAPR-reducing network (PRNet), the bit error rate (BER) and the PAPR of the GFDM system are jointly minimised by adaptively determining the constellation mapping and damping of symbols on each subcarrier and sub-symbol.
ISSN:0250-541X
2250-1754
DOI:10.1007/s40009-023-01230-1