Behavioral Modeling of Rf Power Amplifiers Using Modified Volterra Series

Jin, Zhe; Yan-Wei, Zhou; Song, Zhihuan

doi:10.1142/s0218126609005113

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…To meet rigorous requirements for modern electronic systems, developing efficient modeling method has become an important research topic. According to the type of data needed for extraction, circuit models are divided into two categories: physical models and behavioral models [1]. Physical models are created by analyzing the circuitry of the devices, Journal of Applied Mathematics and Physics while behavioral models characterize the devices in terms of input and output signals, without resorting to their internal constitution.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

Zhe¹,

Kaba²

2021

JAMP

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With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

Zhe¹,

Kaba²

2021

JAMP

Self Cite

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Behavioral modeling of RF power amplifiers based on SVM

Jin

Yan-Wei

2010

The 2nd International Conference on Information Science and Engineering

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RF power amplifiers (PA) are a major source of nonlinearity in a communication system. Accurate behavioral models are indispensable for PA linearization. To describe nonlinear characteristics of power amplifiers, a support vector machine (SVM) based modeling method is presented. The kernel approach and duality theory are employed to train the PA model. Simulation results show that the proposed model provides more accurate prediction of PA output signal compared with classic neural network models.

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Evaluation of an Electrical Equivalent Model Using Measurements and an Optimization Algorithm — Application to a Pyroelectric Sensor

Sifi

Touayar

2015

J CIRCUIT SYST COMP

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A technique to estimate the parameters of an equivalent electrical model of a pyroelectric sensor prototype, based on measurements and an optimization algorithm, is presented. The dynamic behavior predicted by the equivalent electrical model is compared with measurements, and the effect of some geometrical mismatches is investigated as well. The optimization algorithm is a judicious combination of two search methods: random (a uniform statistical distribution) and deterministic (a conjugate gradient method). The reliability of the optimization algorithm to estimate the parameters values of the equivalent electrical model is also studied and discussed. The sensor equivalent electrical model can be used as a quick and intuitive analysis tool to allow the simulation of the device in a system-level design environment.

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Behavioral Modeling of Rf Power Amplifiers Using Modified Volterra Series

Cited by 3 publications

References 3 publications

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

Behavioral modeling of RF power amplifiers based on SVM

Evaluation of an Electrical Equivalent Model Using Measurements and an Optimization Algorithm — Application to a Pyroelectric Sensor

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