Adaptive Bayesian Equalizer with Superimposed Training for Mimo Channels
نویسندگان
چکیده
The Bayesian equalizer is implementable by a proper employment of a radial basis function (RBF) neural network, with the inverse filtering problem posed as a classification problem. The proposed approach allows that the transmission of information and the RBF training be accomplished in a simultaneous and uninterrupted way. Moreover, the channel estimation procedure remains an unimodal optimization problem. Simulation results confirm the effectiveness of the proposed MIMO equalizer.
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