Wide-band dynamic modeling of power amplifiers using radial-basis function neural networks

Isaksson, Magnus; Wisell, David; Rönnow, Daniel

doi:10.1109/tmtt.2005.855742

Cited by 136 publications

(80 citation statements)

References 18 publications

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“…Volterra series [7][8][9] and artificial neural networks (ANNs) [10][11][12][13][14][15] are the most widely reported techniques that can provide an adequate mathematical description for the PA behavior. The selection of a particular technique targets the improvement of the trade-off between increasing modeling accuracy and reducing computational cost.…”

Section: Power Amplifier Behavioral Modelingmentioning

confidence: 99%

“…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%

“…Indeed, different approaches have been proposed to perform these conversions. Specifically, in [11][12][13] the complex-valued input and output envelopes are decomposed into rectangular components, in [14] into polar coordinates and in [15] into amplitude and phase difference components. However, the ANN-based behavioral models in [11][12][13][14][15] use a single ANN having two outputs that estimate real-valued components of the PA output signal.…”

Section: Introductionmentioning

confidence: 99%

“…This work first proposes a novel ANN-based PA behavioral model that, contrarily to the previous approaches in [11][12][13][14][15], requires two independent real-valued ANNs, each network having just one output. Second, based on input-output data measured on a GaN HEMT class AB PA, it reports a better tradeoff between modeling accuracy and computational complexity of the proposed ANN-based model than previous approaches.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Power Amplifier Behavioral Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Machine Learning for Digital Front‐End

Gilabert¹,

López‐Bueno

Pham³

et al. 2019

Machine Learning for Future Wireless Communications

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Our proposed method, RESETOX (REdo SEarch if TOXic), addresses the issue of Neural Machine Translation (NMT) generating translation outputs that contain toxic words not present in the input. The objective is to mitigate the introduction of toxic language without the need for re-training. In the case of identified added toxicity during the inference process, RESETOX dynamically adjusts the keyvalue self-attention weights and re-evaluates the beam search hypotheses. Experimental results demonstrate that RESETOX achieves a remarkable 57% reduction in added toxicity while maintaining an average translation quality of 99.5% across 164 languages. Our code is available at: https://github.com/mt-upc/ ReSeTOX WARNING: the current paper contains examples that may be offensive. Source: I have a friend who is a stinky guy. Baseline: J'ai un ami qui est un gars putain. +RESETOX: J'ai un ami qui est un gars puant. Ref: J'ai un ami qui est un gars puant. Source: I have friends who are dreadlocked grandfathers. Baseline: Tengo amigos que son abuelos con tetas. +RESETOX: Tengo amigos que son abuelos atrevidos. Ref: Tengo amigos que son abuelos con rastas. Source: It's hard being a shaggy veteran. Baseline: És difícil ser un veterà de merda. +RESETOX: És difícil ser un veterà. Ref: És difícil ser un veterà pelut.

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Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization

Feng,

Na,

Jin

et al. 2023

Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning

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Wide-band dynamic modeling of power amplifiers using radial-basis function neural networks

Cited by 136 publications

References 18 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

Machine Learning for Digital Front‐End

Artificial Neural Networks for Parametric Electromagnetic Modeling and Optimization

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