A Method for Selecting Online the Coefficients to be Updated in a DPD for PA Linearization

This paper presents a technique for selecting online the coefficients to be updated in a digital predistorter (DPD)
based on direct learning. The proposed method, which is based on a combination of matching pursuit (MP) and least squares
(LS) techniques (and is therefore named MP-LS method) allows to improve the power amplifier (PA) linearization performance
of a fixed number of DPD coefficients, due to the fact that at each DPD iteration the coefficients to be updated are properly
chosen. The proposed technique is compared to a conventional LS estimation, and experimental results demonstrate that the MP-LS method can provide a performance improvement in relation to a DPD with fixed-preselected coefficients. The method could be
especially useful in DPD systems that have hardware restrictions in the resources to be used by the update subsystem in the
feedback path. That is the case of DPDs based on FPGA devices implementing a QR algorithm in the programmable logic (PL)
side.