The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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ML-Based and Blind Frequency Offset Estimators Robust to Non-Gaussian Noise in OFDM Systems

비정규 잡음에 강인한 ML기반 OFDM 블라인드 주파수 옵셋 추정기

  • 심정윤 (성균관대학교 정보통신대학) ;
  • 윤석호 (성균관대학교 정보통신대학) ;
  • 김광순 (연세대학교 전기전자공학부) ;
  • 이성로 (목포대학교 정보전자공학과)
  • Received : 2013.01.14
  • Accepted : 2013.03.08
  • Published : 2013.04.30
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Abstract

In this paper, we propose robust blind estimators for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise environments. We first propose a maximum likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then, a simpler estimator based on the ML estimator is proposed. From numerical results, we confirm that the proposed estimators are robust to the non-Gaussian noise and have a better estimation performance over the conventional estimator in non-Gaussian noise environments.

본 논문에서는 비정규 잡음에 강인한 직교 주파수 분할 다중화 (orthogonal frequency division multiplexing: OFDM) 블라인드 주파수 옵셋 추정기들을 제안한다. 먼저 복소 등방성 코시 과정으로 모델링 된 비정규 잡음 환경에서 최대 우도 (maximum likelihood: ML) 추정기를 제안한다. 또한, ML 기반의 보다 간단한 추정기를 제안한다. 모의실험을 통해 제안한 추정기들이 비정규 잡음에 강인하며 기존 추정기보다 우수한 주파수 옵셋 추정 성능을 가짐을 보인다.

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References

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