Digital predistortion using direct learning with reduced bandwidth feedback

Digital predistortion (DPD) is a popular approach for linearizing power amplifiers (PAs) in wireless base stations and improving system efficiency. The feedback path in a DPD system typically requires 5x signal bandwidth to accommodate the bandwidth expansion caused by the DPD and nonlinear PA. In this paper, we propose a new approach for adapting DPD parameters using direct learning and reduced bandwidth feedback. The new approach is capable of achieving near full-rate DPD performance and linearization bandwidth with significantly reduced feedback bandwidth. Measurement results on a Doherty PA achieved more than 20 dB corrections over 200 MHz bandwidth for a 2-carrier WCDMA signal spanning 40 MHz with only 81.92 MHz feedback bandwidth.