Shortwave Memory Power Amplifier Linearization Based on Tanh Neural Network Predistorter

Shortwave power amplifiers (PAs) are usually considered as memoryless devices in most existing predistortion techniques. Nevertheless, in shortwave communication systems, PA memory effects can no longer be ignored and memoryless predistortion cannot linearize PAs effectively. By analyzing the characteristics of the power amplifier, an improved predistortion method for memory power amplifier is presented. The Tanh neural network predistorter is used, and its parameters have been adjusted adaptively using an indirect learning architecture. Simulation results show that inter modulation component suppression and compensation for memory effect of power amplifiers have been improved.