  In this study, we propose a differentiable layer for OFDM-based autoencoders
(OFDM-AEs) to avoid high instantaneous power without regularizing the cost
function used during the training. The proposed approach relies on the
manipulation of the parameters of a set of functions that yield complementary
sequences (CSs) through a deep neural network (DNN). We guarantee the
peak-to-average-power ratio (PAPR) of each OFDM-AE symbol to be less than or
equal to 3 dB. We also show how to normalize the mean power by using the
functions in addition to PAPR. The introduced layer admits auxiliary parameters
that allow one to control the amplitude and phase deviations in the frequency
domain. Numerical results show that DNNs at the transmitter and receiver can
achieve reliable communications under this protection layer at the expense of
complexity.
