Dataset Reduction for Neural Network Based Digital Predistorters under Strong Nonlinearities

The artificial neural networks (ANN) are gaining momentum in the digital predistorters (DPD) thanks to their inherently good approximation capabilities. Under strong or complex power amplifier nonlinearities, the size of the ANN can increase and lead to long training periods which are unaffordable in fast-changing waveform scenarios like those proposed for 5G or 6G. In this work we combine the orthogonal matching pursuit technique together with dataset length reduction methods, to significantly shorten the ANN DPD coefficients update time.