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A new channel estimation algorithm capable of exploiting a-priori knowledge about modulation and receive filters and the power delay profile of the physical channel is presented. In order to incorporate the a-priori knowledge into the linear minimum mean square error (LMMSE) channel estimator, a Bayesian approach is applied to an appropriate GSM system model. The performance of the new algorithm is compared to a conventional least squares (LS) channel estimator by means of analysis and simulations. Simulations are carried out for the standard GSM channel profiles (TU50, HT100, RA250). Information about the shape of the power delay profile is assumed to be unavailable to the receiver. The merit of the new channel estimator is confirmed by yielding an MSE approximately 1-2 dB lower than the LS estimator. The lower MSE translates into a BER advantage of approximately 0.25 dB. Exploiting additional knowledge about the shape of the power delay profile results in further BER performance improvements of up to 0.3 dB.