Complexity Suppression of AutoEncoder for PAPR Reduction of OFDM Signals

Orthogonal frequency division multiplexing (OFDM) signals have the characteristic of a high peak-to-average power ratio (PAPR). In this paper, we consider a method to suppress the computational complexity of AutoEncoder, which is a deep learning model, to reduce the PAPR of OFDM signals. We also evaluate the effectiveness of the method through numerical experiments.