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This paper considers semiblind channel estimation and data detection for Orthogonal Frequency-Division Multiplexing (OFDM) over frequency-selective fading channels. The authors show that the samples of an OFDM symbol are jointly complex Gaussian distributed, where the mean and covariance are determined by the locations and values of fixed pilot symbols. They exploit this distribution to derive a novel Maximum-Likelihood (ML) semiblind gradient-descent channel estimator. By exploiting the Channel Impulse Response (CIR) statistics, the authors also derive a semiblind data detector for both Rayleigh and Ricean fading channels. Furthermore, they develop an enhanced data detector, which uses the estimator error statistics to mitigate the effect of channel estimation errors.
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