PAPR Reduction of GFDM Signals Using Encoder-Decoder Neural Network (Autoencoder)
Department
Electrical and Computer Engineering
Document Type
Article
Publication Date
6-1-2023
Abstract
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.
Journal Title
National Academy Science Letters
Journal ISSN
0250541X
Volume
46
Issue
3
First Page
213
Last Page
217
Digital Object Identifier (DOI)
10.1007/s40009-023-01230-1
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