Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929539547922432 |
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| author | Carpi, Fabrizio Rostami, Soheil Cho, Joonyoung Garg, Siddharth Erkip, Elza Zhang, Charlie Jianzhong |
| author_facet | Carpi, Fabrizio Rostami, Soheil Cho, Joonyoung Garg, Siddharth Erkip, Elza Zhang, Charlie Jianzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM Carpi, Fabrizio Rostami, Soheil Cho, Joonyoung Garg, Siddharth Erkip, Elza Zhang, Charlie Jianzhong Information Theory Machine Learning Signal Processing 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. |
| title | Learned Pulse Shaping Design for PAPR Reduction in DFT-s-OFDM |
| topic | Information Theory Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2404.16137 |