Dynamic Behavioral Modeling of 3G Power Amplifiers Using Real-Valued Time-Delay Neural Networks

Liu, Taijun; Boumaiza, Slim; Ghannouchi, Fadhel M.

doi:10.1109/tmtt.2004.823583

Cited by 265 publications

(183 citation statements)

References 15 publications

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“…In literature, there are various techniques that can provide an adequate mathematical description for the PA behavior. Volterra series [7][8][9] and artificial neural networks (ANNs) [10][11][12][13][14][15] are the most widely reported techniques that can simultaneously describe nonlinear and dynamic behaviors. ANNs have the advantage of requiring a lower number of parameters than the Volterra series and have more general validity than polynomial approximations.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs having only real-valued inputs, outputs, weights and biases, are widely reported in literature for the low-pass equivalent behavioral modeling of PAs [11][12][13][14][15]. Since low-pass equivalent behavioral models relate complex-valued envelope signals at the PA input and output, complex-valued signals must be converted into real-valued signals.…”

Section: Introductionmentioning

confidence: 99%

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Low-Pass Equivalent Behavioral Modeling of Rf Power Amplifiers Using Two Independent Real-Valued Feed-Forward Neural Networks

Freire¹,

França²,

Lima³

2014

PIER C

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Abstract-Feed-forward artificial neural networks (ANNs) can provide the adequate model required for the linearization of power amplifiers (PAs) used in wireless communication systems. A common characteristic of previously available ANN-based models for linearization purposes is the use of a single real-valued ANN having two outputs. The contribution of this work is to report the benefits of performing such behavioral modeling based on two independent real-valued ANNs, where each network has a unique output. The proposed ANN-based model is applied to the behavioral modeling of a GaN HEMT class AB PA, and its accuracy is compared to previous approaches in two different scenarios. First, in case of similar number of network parameters, it is observed that the proposed ANN-based model can reduce the normalized mean-square error (NMSE) by up to 1.3 dB. Second, in a situation of comparable modeling accuracy (NMSE = −40 dB), it is observed that the proposed ANN-based model can reduce the number of network parameters by up to 40% (from 62 to 38 real-valued parameters).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Pass Equivalent Behavioral Modeling of Rf Power Amplifiers Using Two Independent Real-Valued Feed-Forward Neural Networks

Freire¹,

França²,

Lima³

2014

PIER C

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“…The neural network approach has also been investigated as one of the modeling and predistortion techniques for PAs because of its adaptive nature and the claim of a universal approximation capability. Different neural topologies and computation algorithms have been proposed [17][18][19][20][21][22]. Now the ANN-based models are seen as a potential alternative to model RF PAs having mediumto-strong memory effects along with high-order nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

“…of accuracy due to its universal approximation capability. Various topologies of ANNs were reported in the literature for PAs behavioral modeling [17][18][19][20][21][22]. In [17], two separate and uncoupled real-valued neural networks were used to model the output amplitude and phase (or the output I and Q components) with the input signal amplitude as the two neural networks' input, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Behavioral Modeling of Rf Power Amplifiers With Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

Yuan¹,

Feng²

2013

PIER C

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Abstract-Behavioral modeling technique provides an efficient and convenient way to analyze and predict the performance of the RF power amplifiers (PAs) in system-level, and thus helps to construct a suitable predistorter to linearize the PA system. To accurately describe the nonlinear dynamic characteristics of PAs, an orthonormal Hermite polynomial basis neural network (OHPBNN) is utilized to represent the PA behavioral model, which outperforms, mainly in respect of modeling accuracy, the classic feedforward neural network using sigmoid activation functions.In addition, we apply an adaptive algorithm to determine the appropriate memory depth of PA behavioral model. Simulation results show that the proposed model provides more accurate prediction of the PAs output signal compared with classic neural network models.

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Digital Predistortion for Microwave Transmitters

Ghannouchi

Hammi

Helaoui

2014

Wiley Encyclopedia of Electrical and Electronics Engineering

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Microwave transmitters comprise the cascade of several subsystems. Among these subsystems, the power amplifier (PA) is identified to be the main source of nonlinearity. Thus, modeling and compensating for the transmitter nonlinearity is often trimmed down to the modeling and compensation of the power amplifier's nonlinearity. Such compensation allows for the use of power‐efficient and nonlinear PA topologies, which results in reducing the energy consumption and cost of wireless transmitters. This article gives an overview of the nonlinear PA behavior and the origins of intermodulation distortions in this subsystem. It defines the most used distortion quantification metrics and the different types of distortions. Power amplifier modeling and digital predistortion principles are explained. The procedure and identification steps of digital predistortion are highlighted, and the different models used in digital predistortion technique are covered. Advanced digital predistortion models for linearizing the impairments in multiple‐input–multiple‐output (MIMO) PAs, dual‐band PAs, and quadrature modulators are also covered.

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Dynamic Behavioral Modeling of 3G Power Amplifiers Using Real-Valued Time-Delay Neural Networks

Cited by 265 publications

References 15 publications

Low-Pass Equivalent Behavioral Modeling of Rf Power Amplifiers Using Two Independent Real-Valued Feed-Forward Neural Networks

Low-Pass Equivalent Behavioral Modeling of Rf Power Amplifiers Using Two Independent Real-Valued Feed-Forward Neural Networks

Behavioral Modeling of Rf Power Amplifiers With Memory Effects Using Orthonormal Hermite Polynomial Basis Neural Network

Digital Predistortion for Microwave Transmitters

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