PAPR Reduction With Pre-Chirp Selection for Affine Frequency Division Multiplexing

Affine frequency division multiplexing (AFDM) is a promising new multicarrier technique for high-mobility communications based on discrete affine Fourier transform (DAFT). By properly tuning two parameters in the DAFT module, the effective channel in the DAFT domain can completely circumvent path overlap, thereby constituting a full representation of delay-Doppler profile. However, AFDM has a crucial problem of high peak-to-average power ratio (PAPR), stemming from randomness of modulated symbols. To reduce the PAPR of AFDM, a novel algorithm named grouped pre-chirp selection (GPS) is proposed in this letter. The GPS varying the pre-chirp parameter across subcarrier groups in a non-enumerated manner and then selects the signal with the smallest PAPR among all candidate signals. We detail the operational procedures of the GPS algorithm, analyzing GPS from four aspects: PAPR performance, computational complexity, spectral efficiency, and bit error rate (BER) performance. Simulations indicate the effectiveness of the proposed GPS in reducing PAPR. At the cost of slight communication performance, AFDM with GPS can achieve better PAPR performance than orthogonal time frequency space (OTFS) while maintaining a lower modulation complexity.

Authors

Institutions

Publication Details

Journal
IEEE Wireless Communications Letters
Published
2024-12-23
DOI
https://doi.org/10.1109/lwc.2024.3521464
Citations
18
Primary Topic
PAPR reduction in OFDM
Type
article
Field-Weighted Citation Impact
3.76

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PAPR Reduction With Pre-Chirp Selection for Affine Frequency Division Multiplexing

Yin Xu, Dazhi He, Xinghao Guo, Haoyang Li et al.
18 citations
IEEE Wireless Communications Letters
PAPR reduction in OFDM
3.76
article

PAPR Reduction With Pre-Chirp Selection for Affine Frequency Division Multiplexing

Yin Xu, Dazhi He, Xinghao Guo, Haoyang Li, Wenjun Zhang, Yao Ge, Tianyao Ma, Haozhi Yuan
article en
18 citations

Abstract

Affine frequency division multiplexing (AFDM) is a promising new multicarrier technique for high-mobility communications based on discrete affine Fourier transform (DAFT). By properly tuning two parameters in the DAFT module, the effective channel in the DAFT domain can completely circumvent path overlap, thereby constituting a full representation of delay-Doppler profile. However, AFDM has a crucial problem of high peak-to-average power ratio (PAPR), stemming from randomness of modulated symbols. To reduce the PAPR of AFDM, a novel algorithm named grouped pre-chirp selection (GPS) is proposed in this letter. The GPS varying the pre-chirp parameter across subcarrier groups in a non-enumerated manner and then selects the signal with the smallest PAPR among all candidate signals. We detail the operational procedures of the GPS algorithm, analyzing GPS from four aspects: PAPR performance, computational complexity, spectral efficiency, and bit error rate (BER) performance. Simulations indicate the effectiveness of the proposed GPS in reducing PAPR. At the cost of slight communication performance, AFDM with GPS can achieve better PAPR performance than orthogonal time frequency space (OTFS) while maintaining a lower modulation complexity.

IEEE Wireless Communications LettersVol. 14(3)
Nanyang Technological University (SG), Shanghai Jiao Tong University (CN), Shandong Jiaotong University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 6%
PAPR reduction in OFDM
3.76
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.