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In this paper, a novel carrier frequencydependent time delay neural network (CF-dependent TDNN) model for behavioral modeling of a memory power amplifier (PA) for multi-band operation is proposed. Traditional methods such as memory polynomial and time delay neural network characterize the nonlinearity of a PA in a single wide band with multiple carriers. However, these methods cannot accurately capture...
Neural networks (NNs) have been widely used in microwave device modeling. One of the greatest challenges is how to speed up the model training process and reduce the development cost. To address the issue, this paper exploits FPGAs to accelerate NN training. Experimental results demonstrate that the model training time can be reduced by up to 99.1%, compared to the traditional software implementation.
This letter presents a novel dynamic Neuro-space mapping (Neuro-SM) technique for nonlinear device modeling. This is an advance over the existing static Neuro-SM which aims to map a given approximate device model towards an accurate model. The proposed technique retains the ability of static Neuro-SM in modifying the effects of nonlinear resistors and current sources. The proposed technique can also...
This paper proposes a novel distributed parallel EM modeling technique to speed up the process of neural network modeling for EM structures. Existing techniques for EM modeling usually need to repeatedly change the parameters of microwave devices and drive the EM simulator to obtain sufficient training and testing samples. As the complexity in EM modeling problem increases, traditional techniques...
In this paper, an advanced Neuro-Space Mapping (SM) modeling technique for nonlinear device modeling is proposed. By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. The novelty of our work is to introduce a Neuro-SM model combining separate mappings for voltage and current...
In this article, a new Neuro-Space mapping method is presented aimed at using neural networks to automatically enhance nonlinear device models, such as FET models. Compared with previously published space mapping methods, our proposed method produces better modeling accuracy and provides more effective combinations of mapping structure with existing coarse model. In our proposed models, separate mappings...
A gallium nitride Doherty power amplifier (GaN Doherty PA) was designed for 2.5 GHz WiMAX band, and a radial-basis function neural network (RBFNN) model is proposed for predicting this amplifier' nonlinear characteristics. Comparison of AM/AM, AM/PM, PAE and Pout curves between the RBFNN model and circuit simulation are given. After 125 epochs, the convergence of this RBFNN model becomes slower and...
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