Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Blind frequency offset estimation method in OFDM systems

OFDM에서 블라인드 주파수 옵셋 추정 방법

  • Received : 2010.08.31
  • Accepted : 2010.09.16
  • Published : 2011.04.30
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Abstract

In this paper, an efficient blind carrier frequency offset (CFO) estimation method in orthogonal frequency division multiplexing (OFDM) systems is proposed. In the proposed method, we obtain two time different received OFDM symbols by using both the cyclic prefix and oversampling technique, and a cost function is defined by using the two OFDM symbols. We show that the cost function can be approximately expressed as a cosine function. Using a property of the cosine function, a formular for estimating the CFO is derived. The estimator of the CFO requires three independent cost function values calculated at three different points of frequency offset. The proposed method is very efficient in computational complexity since no searching operation for the minimum cost value is required. The proposed method reduces 97% of the amount of FFT computation, compared with the ML method. Unlike the conventional methods such as the ML method and the MUSIC] method, the accuracy of the proposed method is independent of the searching resolution since the closed form solution exists. The computer simulation shows that the performance of the proposed method is superior to those of the MUSIC and the ML method.

본 논문은 orthogonal frequency division multiplexing (OFDM) 통신에서 효율적인 블라인드(blind) 주파수 옵셋 추정 방식을 제안한다. 제안된 방식은 오버샘플링과 OFDM 시스템의 cyclic prefix (CP)를 이용하여 시간차가 있는 2개의 OFDM 신호 블록을 얻고 이를 이용하여 블라인드 주파수 옵셋 추정을 위한 비용함수를 정의한다. 본 논문에서는 제안된 비용함수가 코사인 함수로 근사화 될 수 있음을 보였으며 코사인 함수의 기본적인 특성을 이용하여 주파수 옵셋을 추정할 수 있는 폐쇄형(closed form) 추정 공식을 유도하였다. 이 코사인 함수를 이용하면 전체 주파수 옵셋 범위에 대한 탐색 없이 최저 비용함수 값을 쉽게 계산할 수 있기 때문에 주파수 옵셋 추정이 효율적이다. 제안된 방식은 탐색이 필요 없기 때문에 기존의 블라인드 ML 기법보다 계산량이 약 97% 감소하며 컴퓨터 시뮬레이션 결과 평균제곱오차 (mean square error, MSE) 성능이 기존의 ML 기법이나 MUSIC 방식보다 우수함을 보였다.

Keywords

References

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