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.

翻译：高峰均功率比是限制蜂窝系统小区覆盖的主要因素之一，尤其是在上行链路方向。采用频谱扩展频域频谱成形的离散傅里叶变换扩展正交频分复用是降低上行波形峰均比的高效技术之一。本文提出一种基于机器学习的框架，用于确定频域频谱成形滤波器，以优化符号错误率、峰均比和频谱平坦度要求之间的权衡。我们的端到端优化框架考虑了多个重要设计约束，包括奈奎斯特零码间干扰条件。数值结果表明，与常规基准方案相比，学习型频域频谱成形滤波器在符号错误率退化最小的情况下降低了峰均比。优化参数的调整也有助于我们理解用于峰均比抑制的频域频谱成形滤波器的基本局限性和特性。