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In this study, we propose a differentiable layer for OFDM-based autoencoders(OFDM-AEs) to avoid high instantaneous power without regularizing the costfunction used during the training. The proposed approach relies on themanipulation of the parameters of a set of functions that yield complementarysequences (CSs) through a deep neural network (DNN). We guarantee thepeak-to-average-power ratio (PAPR) of each OFDM-AE symbol to be less than orequal to 3 dB. We also show how to normalize the mean power by using thefunctions in addition to PAPR. The introduced layer admits auxiliary parametersthat allow one to control the amplitude and phase deviations in the frequencydomain. Numerical results show that DNNs at the transmitter and receiver canachieve reliable communications under this protection layer at the expense ofcomplexity.

Golay Layer: Limiting Peak-to-Average Power Ratio for OFDM-based Autoencoders