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 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.