The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
조회수
92
피크 대 평균 전력비를 줄이기 위해 부분 전송 시퀀스 기법에서 적합한 벡터를 선택하는 방법을 제안합니다. 기존의 접근 방식에서는 다수의 후보 중에서 적합한 벡터를 선택해야 합니다. 대조적으로, 우리의 방법은 그러한 선택 절차를 포함하지 않고 대신 공분산 행렬이 완화된 문제의 해인 가우스 분포로부터 무작위 벡터를 생성합니다. 적합한 벡터는 무작위 벡터에서 선택됩니다. 이는 기존 방법보다 더 낮은 피크 대 평균 전력 비율을 제공합니다.
Hirofumi TSUDA
T&S inc.
Ken UMENO
the Kyoto University
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부
Hirofumi TSUDA, Ken UMENO, "Randomization Approaches for Reducing PAPR with Partial Transmit Sequence and Semidefinite Relaxation" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 3, pp. 262-276, March 2021, doi: 10.1587/transcom.2019EBP3243.
Abstract: To reduce peak-to-average power ratio, we propose a method of choosing suitable vectors in a partial transmit sequence technique. Conventional approaches require that a suitable vector be selected from a large number of candidates. By contrast, our method does not include such a selecting procedure, and instead generates random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio than a conventional method.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2019EBP3243/_p
부
@ARTICLE{e104-b_3_262,
author={Hirofumi TSUDA, Ken UMENO, },
journal={IEICE TRANSACTIONS on Communications},
title={Randomization Approaches for Reducing PAPR with Partial Transmit Sequence and Semidefinite Relaxation},
year={2021},
volume={E104-B},
number={3},
pages={262-276},
abstract={To reduce peak-to-average power ratio, we propose a method of choosing suitable vectors in a partial transmit sequence technique. Conventional approaches require that a suitable vector be selected from a large number of candidates. By contrast, our method does not include such a selecting procedure, and instead generates random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio than a conventional method.},
keywords={},
doi={10.1587/transcom.2019EBP3243},
ISSN={1745-1345},
month={March},}
부
TY - JOUR
TI - Randomization Approaches for Reducing PAPR with Partial Transmit Sequence and Semidefinite Relaxation
T2 - IEICE TRANSACTIONS on Communications
SP - 262
EP - 276
AU - Hirofumi TSUDA
AU - Ken UMENO
PY - 2021
DO - 10.1587/transcom.2019EBP3243
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2021
AB - To reduce peak-to-average power ratio, we propose a method of choosing suitable vectors in a partial transmit sequence technique. Conventional approaches require that a suitable vector be selected from a large number of candidates. By contrast, our method does not include such a selecting procedure, and instead generates random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio than a conventional method.
ER -