PRNet with Convolution Layer for PAPR Reduction of OFDM Signals

Abstract

This research uses deep learning to address the high peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM), which is critical for wireless communications. Although a PAPR-reducing network (PRNet), which is a deep learning model, can be used to suppress the PAPR, its computational cost is huge. In this research, the number of layers in a PRNet model is optimized and a fully connected layer is replaced with a convolution layer to reduce the computational load.