  High peak-to-average power ratio (PAPR) is one of the main factors limiting
cell coverage for cellular systems, especially in the uplink direction.
Discrete Fourier transform spread orthogonal frequency-domain multiplexing
(DFT-s-OFDM) with spectrally-extended frequency-domain spectrum shaping (FDSS)
is one of the efficient techniques deployed to lower the PAPR of the uplink
waveforms. In this work, we propose a machine learning-based framework to
determine the FDSS filter, optimizing a tradeoff between the symbol error rate
(SER), the PAPR, and the spectral flatness requirements. Our end-to-end
optimization framework considers multiple important design constraints,
including the Nyquist zero-ISI (inter-symbol interference) condition. The
numerical results show that learned FDSS filters lower the PAPR compared to
conventional baselines, with minimal SER degradation. Tuning the parameters of
the optimization also helps us understand the fundamental limitations and
characteristics of the FDSS filters for PAPR reduction.
