Abstract:  The performance of the existing iterative receiver with high computational complexity had to be a big difference from optimal receiver for frequencyselective slow fading channels. In order to improve the performance of the equalizer, the BP-EP-MF messaging passing iterative equalization algorithm was proposed in this paper. The proposed message passing algorithm contained belief propagation (BP), expectation propagation (EP) and mean field (MF). MF was used to handle the nonlinear model of the factor, meanwhile EP made a great contribution for the low complexity implementation of the proposed message passing algorithm iterative receiver. Firstly, a channel factor graph model that based on global posterior probability density function was established. In the channel factor graph model, all unknown variables in the system and the relationship between factor nodes and variable nodes were made clear. Then, according to the characteristics of the factors, reasonable and effective message passing rules and the appropriate message update mechanism were chosen to improve the performance of iterative receiver and reduce the computational complexity. Finally, the data symbols can be estimated based on maximum a posteriori estimation. Simulation results turn out that our message-passing algorithm with little less computational complexity and faster convergence speed leads to better performance than fixedlag soft input nonlinear Kalman filtering (EKF) for frequency-selective slow fading channels.