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.
Power Amplifier (PA) linearization by Digital Pre-Distortion (DPD) using baseband signals is one of the most popular methods for improving wireless transmission systems' efficiency. This research addresses this problem by focusing on reducing the DPD complexity without compromising the linearization system's efficiency. This contribution consists of a DPD structure with memory effects based on the Feed-backed Wiener (FW) system, which results in a pruned Volterra series. A FIR filter is used as a feedback path to compensate for the PA memory effects. The proposed structure is used to linearize an LDMOS PA (50 W -500 MHz to 2.5 GHz) in the case of LTE/4G signals with different bandwidths and output powers. A comparison in terms of DPD identification and PA linearization performances between the proposed model and the Generalized Memory Polynomial (GMP) model shows that the proposed model presents similar performances, with the advantage of reduced complexity.
Digital predistortion (DPD) using baseband signals is commonly used for power amplifier linearization. This paper is devoted to this subject and aims to reduce DPD complexity. In this study, we propose a structure that allows to decrease the number of DPD parameters by using multiple blocks, with each one of them dedicated to characterizing the non-linear behavior and/or memory effects. Such a structure is based on the feedback Wiener system, involving a FIR filter used as a feedback path to reproduce the PA inverse dynamics. A memory polynomial block (MP) is inserted as the final element to minimize the modeling errors. A relevant model identification method, based on an iterative algorithm, has been developed as well. The proposed architecture is used for the linearization of a commercial class-AB LDMOS RF PA by NXP Semiconductors, in wideband communication systems. Comparison of performance with the conventional generalized memory polynomial model (GMP) shows that the proposed model offers similar results, with its advantage consisting in the reduced number of parameters.
The time reversal (TR) techniques used in electromagnetics have been limited for a variety of reasons, including extensive computations, complex modeling and simulation, processes as well as, large-scale numerical analysis. In this paper, the SIBC-FDTD method is applied to address these issues and to efficiently model TR systems. An original curvilinear modeling method is also proposed for constructing various obstacles in a 2D microwave cavity and for processing the corners of the cavity. The EM waves’ spatio-temporal focalization has been realized, and results of the simulations further prove the accuracy and effectiveness of this modeling method. Furthermore, they demonstrate that the microwave cavity processes may significantly improve the focalization quality in terms of SSLL enhancement.
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