ML-Based and Blind Frequency Offset Estimators Robust to Non-Gaussian Noise in OFDM Systems 


Vol. 38,  No. 4, pp. 365-370, Apr.  2013


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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.

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  Cite this article

[IEEE Style]

J. Shim, S. Yoon, K. S. Kim, S. R. Lee, "ML-Based and Blind Frequency Offset Estimators Robust to Non-Gaussian Noise in OFDM Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 4, pp. 365-370, 2013. DOI: .

[ACM Style]

Jeongyoon Shim, Seokho Yoon, Kwang Soon Kim, and Seong Ro Lee. 2013. ML-Based and Blind Frequency Offset Estimators Robust to Non-Gaussian Noise in OFDM Systems. The Journal of Korean Institute of Communications and Information Sciences, 38, 4, (2013), 365-370. DOI: .

[KICS Style]

Jeongyoon Shim, Seokho Yoon, Kwang Soon Kim, Seong Ro Lee, "ML-Based and Blind Frequency Offset Estimators Robust to Non-Gaussian Noise in OFDM Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 4, pp. 365-370, 4. 2013.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880