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.

在本研究中，我们提出了一种基于机器学习的框架，用于确定频域频谱塑形扩展技术（FDSS）滤波器，通过在符号误码率（SER）、峰均功率比（PAPR）和频谱平坦度之间进行权衡来优化。通过调整优化参数，我们了解了FDSS滤波器在PAPR降低方面的根本限制和特性。

DFT-s-OFDM中的PAPR减少的学习脉冲塑形设计