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

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 a...

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Hauptverfasser: Abi Hussein, Mazen, Bohara, V. A., Venard, O.
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Venard, O.
description 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).
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subjects Computer architecture
Convergence
Digital predistortion
high power amplifiers
non-linear filters
Nonlinear distortion
Nonlinear systems
Predistortion
Signal to noise ratio
title On the system level convergence of ILA and DLA for digital predistortion
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