Low-PAPR Rank-2 DFT-s-OFDM Waveform for 6G Uplink
Abstract
Rank-2 discrete Fourier transform-spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is attracting renewed interest for 6G uplink, offering not only spatial multiplexing gains but also an additional degree of freedom for peak-to-average power ratio (PAPR) reduction. We exploit this freedom by selectively exchanging constellation symbols between the two layers before DFT spreading, preserving the transmitted information while reshaping the resulting waveforms. Building on this idea, we propose Cyclic Layer Assignment with Shared-basis Synthesis (CLASS). CLASS employs carefully designed cyclic assignment patterns to generate diverse waveform candidates, while reusing a small number of waveform components to avoid synthesizing each candidate separately. We further establish the optimality of the cyclic design in maximizing waveform diversity under a minimum-basis constraint when equal group sizes are feasible. With only six inverse DFTs, CLASS explores 16 assignments and reduces the 99th-percentile worst-layer PAPR by 1.41 dB relative to fixed assignment, outperforming representative selected mapping (SLM) and partial transmit sequences (PTS) schemes under the same synthesis budget.
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BibTeX
@misc{cao2026lowpapr,
title = {{Low-PAPR Rank-2 DFT-s-OFDM Waveform for 6G Uplink}},
author = {Yewen Cao and Fen Hou and Yulin Shao},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00862},
eprint = {2611.00862},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00862},
}
RIS
TY - UNPB
TI - Low-PAPR Rank-2 DFT-s-OFDM Waveform for 6G Uplink
AU - Yewen Cao
AU - Fen Hou
AU - Yulin Shao
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00862
UR - https://arxiv.org/abs/2611.00862
AB - Rank-2 discrete Fourier transform-spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is attracting renewed interest for 6G uplink, offering not only spatial multiplexing gains but also an additional degree of freedom for peak-to-average power ratio (PAPR) reduction. We exploit this freedom by selectively exchanging constellation symbols between the two layers before DFT spreading, preserving the transmitted information while reshaping the resulting waveforms. Building on this idea, we propose Cyclic Layer Assignment with Shared-basis Synthesis (CLASS). CLASS employs carefully designed cyclic assignment patterns to generate diverse waveform candidates, while reusing a small number of waveform components to avoid synthesizing each candidate separately. We further establish the optimality of the cyclic design in maximizing waveform diversity under a minimum-basis constraint when equal group sizes are feasible. With only six inverse DFTs, CLASS explores 16 assignments and reduces the 99th-percentile worst-layer PAPR by 1.41 dB relative to fixed assignment, outperforming representative selected mapping (SLM) and partial transmit sequences (PTS) schemes under the same synthesis budget.
ER -