Abstract
In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as -45.3dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as -46.1dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data.
Highlights
The power amplifier (PA) is a major device included in a transceiver system
Static Distortions/Deep Neural Network Model: The second model proposed in this work shares similarity with the first model in the sense that the same static distortions sub-model is used for the accurate modeling of the Doherty Power Amplifier (DPA) memoryless nonlinearity profile, a different sub-model is introduced for the modeling of the memory effects of the device under test
2) Apply fitting algorithm to calculate the parameters of the static AM-AM model ( a1 through a7 ) from equation (1), 3) Apply fitting algorithm to calculate the parameters of the static AM-PM model ( b1 through b9 ) from equations (2)
Summary
The power amplifier (PA) is a major device included in a transceiver system. Its performance significantly impacts the quality of the transmitted signal and that of the communication link [1]-[4]. There are many techniques and models developed for linearizing PAs. Each model has a different structure, complexity (number of model parameters) and error performance. Bidirectional long-short-term memory networks (BiLSTM) have been introduced in the literature [15] This architecture outperforms many current deep learning techniques for DPD and PA modeling especially when the PA exhibits strong memory effects. Equations are used to determine the behavior of the PA along with its memory effects based on the measured input and output data Those models range from different forms of complex-valued polynomial models to real-valued neural network-based models. The principal contributions of this paper are as follows: 1) Develop low complexity and high performance models for the memoryless AM-AM and AM-PM characteristics of GaN based DPA. The proposed modeling process is described and the obtained results are reported and discussed
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