  This paper introduces the architecture of a convolutional autoencoder (CAE)
for the task of peak-to-average power ratio (PAPR) reduction and waveform
design, for orthogonal frequency division multiplexing (OFDM) systems. The
proposed architecture integrates a PAPR reduction block and a non-linear high
power amplifier (HPA) model. We apply gradual loss learning for multi-objective
optimization. We analyze the models performance by examining the bit error rate
(BER), the PAPR and the spectral response, and comparing them with common PAPR
reduction algorithms.
