Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM

TL;DR

This paper introduces a machine learning-based method to design frequency-domain spectrum shaping filters for DFT-s-OFDM, effectively reducing PAPR while maintaining spectral and error rate performance in uplink cellular systems.

Contribution

It presents an end-to-end optimization framework for learned FDSS filters that balances PAPR reduction, SER, and spectral flatness, considering practical constraints.

Findings

Learned FDSS filters outperform traditional methods in PAPR reduction.

The approach maintains low SER with minimal degradation.

Parameter tuning reveals fundamental limits of PAPR reduction techniques.

Abstract

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.