On the system level convergence of ILA and DLA for digital predistortion

Abstract

In this paper, we present the results for system level convergence of indirect learning architecture (ILA) and direct learning architecture (DLA) for digital predistortion. We show that best performance with ILA and DLA can only be obtained if the system level identification of the power amplifier and predistorter is done iteratively. Results are demonstrated in terms of improvement in adjacent channel power ratio (ACPR) and error vector magnitude (EVM) at the output of power amplifier (PA) with each system level iteration for both the architectures when a Long Term Evolution-Advanced (LTE-Advanced) signal is applied at the input. We also show that predistorter identification with DLA is more robust compared to ILA in presence of additive white Gaussian noise (AWGN).