Iterative Approach to the Indirect Learning Architecture for Baseband Digital Predistortion

Power amplifiers, an integral component to most wireless communication systems, are inherently nonlinear devices that introduce out-of-band spectral regrowth and constellation degradation to the amplifier output. One solution is to operate in a highly backed-off state to achieve quasi-linear performance. Operating in this region requires a higher-saturated power rating for a given output power and reduces DC-to-RF efficiency. A more effective solution is to apply digital predistortion which pre-compensates for the harmful nonlinear effects. A common framework for identifying the predistortion system is known as the indirect learning architecture. A single iteration of this architecture, i.e. a one-step approach, demonstrates performance improvement. We propose an multi- iteration approach to the indirect learning architecture that demonstrates additional improvement, as exhibited by three different algorithms shown to be effective at identifying a predistorter.