A method for selecting online the coefficients to be updated in a DPD for PA linearization

Abstract

This paper presents a technique for selecting onlinethe coefficients to be updated in a digital predistorter (DPD)based on direct learning. The proposed method, which is basedon a combination of matching pursuit (MP) and least squares(LS) techniques (and is therefore named MP-LS method) allowsto improve the power amplifier (PA) linearization performanceof a fixed number of DPD coefficients, due to the fact that ateach DPD iteration the coefficients to be updated are properlychosen. The proposed technique is compared to a conventional LSestimation, and experimental results demonstrate that the MP-LSmethod can provide a performance improvement in relation toa DPD with fixed-preselected coefficients. The method could beespecially useful in DPD systems that have hardware restrictionsin the resources to be used by the update subsystem in thefeedback path. That is the case of DPDs based on FPGA devicesimplementing a QR algorithm in the programmable logic (PL)side