The Mobile Communication Channels present two difficult problems of System theory: time-varying transfer functions (due to mobility) and non-linearity (mainly due to satellite segments in the channel).


Quite a few techniques are able to cope with this kind of situation, mainly when real time processing constraints are added. This paper intends to show that Neural Networks (NN) are able to provide very flexible and efficient solutions to two important classes of problems in the field: the mobile channel identification, aiming to provide a NN channel model; and the equalization problem, which intends to ‘inverse’ the channel in some sense.