A comparison of indirect learning and closed loop estimators used in digital predistortion of power amplifiers

Abstract

Indirect learning is often used as an estimator in digital predistortion of power amplifiers (PAs). The estimator has inherent flaws that become apparent when signal bandwidths increase. These include coefficient offsets, excessive ADC sampling requirements, and susceptibility to EVM and PA saturation. A comparison to the closed loop estimator shows that these flaws are specific to the indirect learning estimator.