The use of a Neural Net for Copeing with Nonlinear Distortions

In this paper a predistorter realized with a neural net is proposed. The predistortion acts on the whole input waveform : it provides a continuous time prewarping where convenctional base band predistortion acts only on the discrete time input sequence. The overall linearity of the transmitter is so recovered and the nonlinear ISI is removed at sampling point The work is a development of a previous one [10] in which a neural net was employed to realize a base-band memoryless predistortion. The results obtained for 16, 64 and 256 QAM modulation systems show excellent performance if compared with base-band memoryless predistortion expecially if small roll-off factors are employed. The major improvement, however, is represented by the extreme simplicity of the proposed solution compared with convenctional predistorters with memory. In this work we have assumed some simplification concerning the neural net architecture; future works, however, will be aimed to removing these reductive assumptions.