Subband digital predistorsion based on Indirect Learning Architecture

Abstract

This paper deals with the linearization of RF power amplifiers (PAs) using digital predistortion (DPD) technique. One of the most important constraint on DPD implementation is digitization of PA output signal needed for identification of predistorter model. The bandwidth of this signal may be 3 to 7 times wider than the bandwidth of the input signal. The sampling rate required for accurate compensation of out-of-band distortions is thus very high, and has a direct impact on power consumption and implementation complexity of DPD identification algorithms on digital processor. In this paper, we propose a new iterative DPD identification algorithm based on the Indirect Learning Architecture (ILA) and on subband decomposition of PA output signal. The proposed algorithm converges to conventional ILA solution with a drastic decrease in required sampling rate.