Abstract: 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.
Abstract: In this paper, frequency offset (FO) estimation schemes
robust to the non-Gaussian noise environments are proposed for
orthogonal frequency division multiplexing (OFDM) systems. First,
a maximum-likelihood (ML) estimation scheme in non-Gaussian
noise environments is proposed, and then, the complexity of the
ML estimation scheme is reduced by employing a reduced set of
candidate values. In numerical results, it is demonstrated that the
proposed schemes provide a significant performance improvement
over the conventional estimation scheme in non-Gaussian noise
environments while maintaining the performance similar to the
estimation performance in Gaussian noise environments.