A data-aided EVM estimator for SNR utilizing Zadoff-Chu sequence as preamble

Xiuhua Li; Yonggang Chi; Xuezhi Tan; Yangyang Zhao

doi:10.1016/j.proeng.2012.01.006

A data-aided EVM estimator for SNR utilizing Zadoff-Chu sequence as preamble

Xiuhua Li^*
, Yonggang Chi
, Xuezhi Tan
, Yangyang Zhao

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology
Science and Technology on Communication Networks Laboratory

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

A novel data-aided error vector magnitude (EVM) estimator for signal-to-noise ratio (SNR) is derived. Previous SNR estimators are mainly based on the data symbols or the preamble symbols. However, most existing EVM estimators need the factually received symbols modulated and even coded again to recover the symbols in the receiver. To improve it, a good new estimator that utilizes Zadoff-Chu sequence as the preamble is recommended and adopted in this paper. Its performance is examined and compared with Maximum-Likelihood (ML) estimator by the measures of the normalized bias and the normalized mean square error. Numerical results are presented to show the visible advantages obtained by using this estimator.

Original language	English
Pages (from-to)	573-578
Number of pages	6
Journal	Procedia Engineering
Volume	29
DOIs	https://doi.org/10.1016/j.proeng.2012.01.006
State	Published - 2012
Event	2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 - Harbin, China Duration: 10 Mar 2012 → 11 Mar 2012

Keywords

Error vector magnitude
Estimation
Signal-to-noise ratio
Zadoff-Chu sequence

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10.1016/j.proeng.2012.01.006

Cite this

@article{e71770f7abfd4a0abcd0d0006991fdfe,

title = "A data-aided EVM estimator for SNR utilizing Zadoff-Chu sequence as preamble",

abstract = "A novel data-aided error vector magnitude (EVM) estimator for signal-to-noise ratio (SNR) is derived. Previous SNR estimators are mainly based on the data symbols or the preamble symbols. However, most existing EVM estimators need the factually received symbols modulated and even coded again to recover the symbols in the receiver. To improve it, a good new estimator that utilizes Zadoff-Chu sequence as the preamble is recommended and adopted in this paper. Its performance is examined and compared with Maximum-Likelihood (ML) estimator by the measures of the normalized bias and the normalized mean square error. Numerical results are presented to show the visible advantages obtained by using this estimator.",

keywords = "Error vector magnitude, Estimation, Signal-to-noise ratio, Zadoff-Chu sequence",

author = "Xiuhua Li and Yonggang Chi and Xuezhi Tan and Yangyang Zhao",

year = "2012",

doi = "10.1016/j.proeng.2012.01.006",

language = "英语",

volume = "29",

pages = "573--578",

journal = "Procedia Engineering",

issn = "1877-7058",

publisher = "Elsevier Ltd",

note = "2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 ; Conference date: 10-03-2012 Through 11-03-2012",

}

TY - JOUR

T1 - A data-aided EVM estimator for SNR utilizing Zadoff-Chu sequence as preamble

AU - Li, Xiuhua

AU - Chi, Yonggang

AU - Tan, Xuezhi

AU - Zhao, Yangyang

PY - 2012

Y1 - 2012

N2 - A novel data-aided error vector magnitude (EVM) estimator for signal-to-noise ratio (SNR) is derived. Previous SNR estimators are mainly based on the data symbols or the preamble symbols. However, most existing EVM estimators need the factually received symbols modulated and even coded again to recover the symbols in the receiver. To improve it, a good new estimator that utilizes Zadoff-Chu sequence as the preamble is recommended and adopted in this paper. Its performance is examined and compared with Maximum-Likelihood (ML) estimator by the measures of the normalized bias and the normalized mean square error. Numerical results are presented to show the visible advantages obtained by using this estimator.

AB - A novel data-aided error vector magnitude (EVM) estimator for signal-to-noise ratio (SNR) is derived. Previous SNR estimators are mainly based on the data symbols or the preamble symbols. However, most existing EVM estimators need the factually received symbols modulated and even coded again to recover the symbols in the receiver. To improve it, a good new estimator that utilizes Zadoff-Chu sequence as the preamble is recommended and adopted in this paper. Its performance is examined and compared with Maximum-Likelihood (ML) estimator by the measures of the normalized bias and the normalized mean square error. Numerical results are presented to show the visible advantages obtained by using this estimator.

KW - Error vector magnitude

KW - Estimation

KW - Signal-to-noise ratio

KW - Zadoff-Chu sequence

UR - https://www.scopus.com/pages/publications/84863369914

U2 - 10.1016/j.proeng.2012.01.006

DO - 10.1016/j.proeng.2012.01.006

M3 - 会议文章

AN - SCOPUS:84863369914

SN - 1877-7058

VL - 29

SP - 573

EP - 578

JO - Procedia Engineering

JF - Procedia Engineering

T2 - 2012 International Workshop on Information and Electronics Engineering, IWIEE 2012

Y2 - 10 March 2012 through 11 March 2012

ER -

A data-aided EVM estimator for SNR utilizing Zadoff-Chu sequence as preamble

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Keywords

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