Joint blind symbol rate estimation and data symbol detection for linearly modulated signals

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3 Citations (Scopus)

Abstract

This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearly modulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the log-likelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the inter-symbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kaiman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients.

Original languageEnglish
Pages (from-to)101-107
Number of pages7
JournalJournal of Communications Software and Systems
Volume5
Issue number3
Publication statusPublished - 2010
Externally publishedYes

Fingerprint

Detectors
Demodulation
Fading channels
Probability density function
Sampling

Keywords

  • Cyclostationarity
  • Data symbol detection
  • Expectation maximization
  • Particle filter
  • Symbol rate estimation

ASJC Scopus subject areas

  • Software
  • Electrical and Electronic Engineering

Cite this

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title = "Joint blind symbol rate estimation and data symbol detection for linearly modulated signals",
abstract = "This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearly modulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the log-likelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the inter-symbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kaiman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients.",
keywords = "Cyclostationarity, Data symbol detection, Expectation maximization, Particle filter, Symbol rate estimation",
author = "Sangwoo Park and Erchin Serpedin and Khalid Qaraqe",
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journal = "Journal of Communications Software and Systems",
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TY - JOUR

T1 - Joint blind symbol rate estimation and data symbol detection for linearly modulated signals

AU - Park, Sangwoo

AU - Serpedin, Erchin

AU - Qaraqe, Khalid

PY - 2010

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N2 - This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearly modulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the log-likelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the inter-symbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kaiman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients.

AB - This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearly modulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the log-likelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the inter-symbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kaiman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients.

KW - Cyclostationarity

KW - Data symbol detection

KW - Expectation maximization

KW - Particle filter

KW - Symbol rate estimation

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