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With the increasing demand for large signal bandwidth in communication systems, the complexity of the behavioral model of power amplifier (PA) will also increase. In this paper, a modified weighted magnitude‐selective model based on magnitude‐selective affine (MSA) function based model and weighted memory polynomial model is proposed to ensure low complexity and good modeling performance under broadband signal excitation. This model is constructed by using the power‐dependent weighted function instead of the sum of multiple MSA functions in MSA. It can not only be used to solve the nonlinearity of PA excited by large bandwidth signal but also reducing the model coefficients without affecting the modeling performance makes the digital predistortion of large bandwidth signals more simple and effective. In this paper, a 100 MHz broadband signal is used for simulation and experiment on a wideband PA. The results show that for the same performance the WMA model required approximately 80% less coefficients. [ABSTRACT FROM AUTHOR]

To cope with the degradation of digital predistortion when bandwidth changes, a bandwidth adaptive behavioral model with dynamic structures for radio frequency (RF) power amplifiers (PAs) is proposed in this article. By applying the nonlinear post‐compensation technology, PA's nonlinearity is decomposed and analyzed. Short‐time Fourier transform and transfer functions are adopted to study the influence of bandwidth variation on the linear memory effect. Based on PA's memory effect analysis, a bandwidth adaptive model with dynamic structures and a coefficient update scheme with low complexity are proposed. Experiments carried on PAs show that the proposed model achieves similar linearization performance as other conventional models. Simultaneously, the proposed model has fewer coefficients and a reduction in the coefficient update of at least 50%. [ABSTRACT FROM AUTHOR]

An under-sampling digital predistortion based on a multi-tone mixing feedback technique (MTM-DPD) is presented in this article. In contrast to the under-sampling digital predistortion (DPD) based on conventional receivers, the proposed method could simultaneously reduce the sampling rate and acquisition bandwidth using a multi-tone mixing (MTM)-based feedback channel. The MTM-DPD is implemented with an evolved direct learning architecture, where the coefficients of a predistorter are estimated based on the aliased feedback signal, preprocessed input signal, and preprocessed kernels of the DPD model. To preprocess the input signal and kernels, the behavioral model of the MTM-based feedback channel and its corresponding identification algorithm are analyzed in detailed. Experimental results show that for a gallium nitride (GaN)-based power amplifier (PA) with 40-MHz input, under the excitation of a 17 tone LO signal with frequency interval 7.68 MHz, the MTM-DPD with sampling rate 15.36 MSPS and acquisition bandwidth 15.36 MHz could improve the adjacent channel power ratio (ACPR) from −30.1 to −46.5 dBc and the error vector magnitude (EVM) from 9.13% to 1.76%. Compared with the conventional full sampling DPD (FS-DPD), the MTM-DPD could reduce the cost of analog-to-digital converters (ADCs) in feedback channel significantly while maintaining satisfactory linearization performance. [ABSTRACT FROM AUTHOR]