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We analyze the problem of joint channel-data estimation in fast fading channels. We propose a hybrid structure which associates the Kalman filter and particle filtering, respectively, for the channel and data estimation. We compare this solution with the classical reduced complexity methods. We show that the application of particle filtering to the discrete state space of the data leads to an approach similar to the T algorithm. Hence, this method cannot improve the trade-off between performance and computational complexity of the classical solutions. We conclude that it is preferable to use particle filtering for the joint estimation of discrete and continuous parameters.