Analysis on LUT based digital predistortion using direct learning architecture for linearizing power amplifiers

In modern wireless communication system, power amplifier (PA) is an important component which is expected to be operated at the region of high power efficiency, but in this region, PA is inherently nonlinear. Thus, the linearization of high power efficient PA is necessary. In this paper, direct learning architecture (DLA) and indirect learning architecture (ILA) are firstly compared. It shows that DLA is more robust than ILA. Then a baseband digital predistortion (DPD) method with DLA is proposed for power amplifier linearization based on combined look-up tables (LUT) and memory polynomial (MP) model. The main innovation is that a LUT-based approach is proposed to calculate directly the complex-valued predistorted signal. Moreover, some interpolation techniques are introduced to reduce the LUT size. The proposed DPDs are validated experimentally. Additionally, the influences of some important parameters in experimental setup, such as the number of bits of analog-to-digital converter (ADC) and the instrument bandwidth, are analyzed.

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