A new digital predistortion using indirect learning with constrained feedback bandwidth for wideband power amplifiers

Abstract

Digital predistortion (DPD) is one of the most effective techniques to mitigate the power amplifier (PA) nonlinear distortion. The DPD feedback bandwidth is often restricted by the nonideal electronic components, e.g., the anti-aliasing filter, which introduces bandwidth mismatch between model basis function and feedback signal thus degrades the linearization performance. This paper presents a new DPD solution for wideband PA systems with constrained feedback bandwidth. By including a linear operation into the PA identification, the PA model can be accurately estimated. Subsequently, the DPD parameters are extracted using the PA model estimated output and PA input signal by applying indirect learning algorithm. Experiments demonstrate a 23-dB adjacent channel leakage ratio improvement is acquired on a 100-MHz Long Term Evolution-advanced signal with the feedback bandwidth reduced from 500 MHz to 140 MHz.