This paper presents a novel adaptive digital predistortion (DPD) technique based on a cascade of an adaptive indirect learning architecture (ILA) and a static direct learning architecture (DLA) using a linear interpolation look-up-table (LILUT). The static LILUT-DLA-based DPD is designed to identify the inverse of a radio-frequency power amplifier (PA) model. The cascaded system of the DLA-based predistorter (PD) and PA is theoretically linear. However, in real-time applications, the PA characteristics change with time due to process, supply voltage, and temperature variations, making this cascaded system not strictly linear, which results in some residual nonlinear distortion at the PA output. This residual distortion is effectively compensated by an additional adaptive ILA-based PD using least mean squares or recursive least squares. Thanks to the incorporation of the static DLA, the proposed DPD approach is less sensitive to the PA output noise, ensuring a better preinverse of the PA and also requiring a smaller number of adaptive coefficients than either the adaptive stand-alone DLA-or ILA-based DPDs. The experimental results show that the proposed DPD technique effectively linearizes the PA, even if its characteristics change, and obtains better linearization performance than either the classical stand-alone DLA-or stand-alone ILA-based DPDs.
UHF (225 MHz -400 MHz) and VHF (118 MHz -144 MHz) transmitters in aeronautical domain are among the most strict telecommunication equipments. ICAO (International Civil Aviation Organization) forcasts 4% traffic growth over the coming 15 years. Due to this evolution, the data exchange between the planes and the infrastructure increases more and more resulting in a saturation of the frequency bands allocated to this service. The main purpose of this paper is to describe Power Amplifier (PA) conception challenges. Distant noise (to carrier) issue is resolved by the right PA's structure choice. Reliable and well-chosen linearization method is necessary in close noise (to carrier) significant reduction.
In this work, a new adaptive digital predistorter (DPD) is proposed to linearize radio frequency power amplifiers (PA). The DPD structure is composed of two sub-models. A Feedback–Wiener sub-model, describing the main inverse nonlinearities of the PA, combined with a second sub-model based on a memory polynomial (MP) model. The interest of this structure is that only the MP model is identified in real time to compensate deviations from the initial behavior and thus further improve the linearization. The identification architecture combines offline measurement and online parameter estimation with small number of coefficients in the MP sub-model to track the changes in the PA characteristics. The proposed structure is used to linearize a class AB 75 W PA, designed by Telerad society for aeronautical communications in Ultra High Frequency (UHF) / Very High Frequency (VHF) bands. The obtained results, in terms of identification of optimal DPD and the performances of the digital processing, show a good trade-off between linearization performances and computational complexity.
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