摘要
To compensate for nonlinear distortion introduced by RF power amplifiers （PAs） with memory effects, two correlated models, namely an extended memory polynomial （EMP） model and a memory lookup table （LUT） model, are proposed for predistorter design. Two adaptive digital predistortion （ADPD） schemes with indirect learning architecture are presented. One adopts the EMP model and the recursive least square （RLS） algorithm, and the other utilizes the memory LUT model and the least mean square （LMS） algorithm. Simulation results demonstrate that the EMP-based ADPD yields the best linearization performance in terms of suppressing spectral regrowth. It is also shown that the ADPD based on memory LUT makes optimum tradeoff between performance and computational complexity.
To compensate for nonlinear distortion introduced by RF power amplifiers （PAs） with memory effects, two correlated models, namely an extended memory polynomial （EMP） model and a memory lookup table （LUT） model, are proposed for predistorter design. Two adaptive digital predistortion （ADPD） schemes with indirect learning architecture are presented. One adopts the EMP model and the recursive least square （RLS） algorithm, and the other utilizes the memory LUT model and the least mean square （LMS） algorithm. Simulation results demonstrate that the EMP-based ADPD yields the best linearization performance in terms of suppressing spectral regrowth. It is also shown that the ADPD based on memory LUT makes optimum tradeoff between performance and computational complexity.
