Autoencoder Based PAPR Reduction for OTFS Modulation

Orthogonal time frequency space (OTFS) modulation shows significant advantages over orthogonal frequency division multiplexing (OFDM), specially in environments with high frequency dispersion. However, high peak-to-average power ratio (PAPR) has been one of the major drawbacks of OTFS systems, which impairs the efficiency of the power amplifier. To resolve the problem, we propose a PAPR reduction method based on the autoencoder (AE) architecture through deep learning (DL) techniques, where the encoder is trained to reduce the PAPR and the decoder is trained to reconstruct the original signal. By carefully designing the loss function, the bit error rate (BER) and the PAPR are simultaneously minimized, and a hyper-parameter is introduced to achieve a good compromise between BER and PAPR in the proposed scheme. Simulation results validate the advantages of the proposed scheme as compared to the other conventional schemes.