ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation
schemes robust to the non-Gaussian noise for orthogonal frequency
division multiplexing (OFDM) systems. A maximum-likelihood (ML)
scheme and a low-complexity estimation scheme are proposed by
applying the probability density function of the cyclic prefix of
OFDM symbols to the ML criterion. From simulation results, it is
confirmed that the proposed schemes offer a significant FO estimation
performance improvement over the conventional estimation scheme
in non-Gaussian noise environments.

• Keunhong Chae
• Seokho Yoon

• Frequency offset
• cyclic prefix
• maximum-likelihood
• non-Gaussian noise
• OFDM.

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