Reliable comparison for power amplifiers nonlinear behavioral modeling based on regression trees and random forest

Aguila-Torres, Daniel Santiago; Galaviz-Aguilar, José Alejandro; Cárdenas-Valdez, José Ricardo

doi:10.1109/iscas48785.2022.9937863

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…The RF algorithm selects a random subset of size k from the attribute set at each node of the base decision tree, and then the optimal attribute is selected from this subset. 50 Meanwhile, ERT provide an additional level of randomness. With an increase in randomness, the algorithm can reduce variance at the expense of a slight increase in bias.…”

Section: Bootstrap Aggregating Model (Bam)mentioning

confidence: 99%

“…The complexity of the problem determines the number of intermediate nodes and layers. As a result, it terminates at leaf nodes containing predicted values 49,50 . The simplicity of their structure makes DTs easy to operate.…”

Section: Model Development Frameworkmentioning

confidence: 99%

“…A major difference between the RF algorithm and the DT‐based BAM model is the introduction of random attribute selection during training. The RF algorithm selects a random subset of size k from the attribute set at each node of the base decision tree, and then the optimal attribute is selected from this subset 50 . Meanwhile, ERT provide an additional level of randomness.…”

Section: Model Development Frameworkmentioning

confidence: 99%

“…As a result, it terminates at leaf nodes containing predicted values. 49,50 The simplicity of their structure makes DTs easy to operate. Nevertheless, DT's structural characteristics limit their ability to interpolate.…”

Section: Random Search Optimization-tuned Decision-tree Modelmentioning

confidence: 99%

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Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Liu,

Cai

2023

Int J Numerical Modelling

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A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large‐signal behavior, machine learning‐based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning‐based large‐signal modeling techniques is presented in this article. To develop large‐signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm‐assisted ANN (GA‐ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five‐fold cross‐validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed.

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Section: Bootstrap Aggregating Model (Bam)mentioning

confidence: 99%

Section: Model Development Frameworkmentioning

confidence: 99%

Section: Model Development Frameworkmentioning

confidence: 99%

Section: Random Search Optimization-tuned Decision-tree Modelmentioning

confidence: 99%

See 2 more Smart Citations

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Liu,

Cai

2023

Int J Numerical Modelling

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“…This modeling method is a flexible technique without extensive parametric tuning. Regression trees can use different methods to calculate the node separations and determine the depth of the tree [35].…”

Section: Machine Learning (Makine öğRenmesi)mentioning

confidence: 99%

Fleet Type Planning for Private Air Transport After Covid-19

Kılıç

Albayrak

Erkayman

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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The global impact of the epidemic COVID-19 has done great damage to air transport. Demand for airline transportation has declined for reasons such as quarantine practices by countries, curfews, the economic recession, and the transfer of meetings to digital platforms. This situation has also led to a change in individuals' preferences for air transport. The most striking change in air transport is the tendency of individuals to private air transport privately to minimize the health risks that may arise from personal contacts. Individuals who avoid commercial air transport where public transportation is has transitioned private air transport. For these reasons, an forecasting study was conducted in this study so that a private airline company can provide accurate flight plans in the future. For the forecast study, the number of aircraft types for 2022 was determined by obtaining data on the number of aircraft by passenger capacity, the number of flights, and the number of passengers for 2019-2021 from the airline company. In the forecasting study, the models with the highest accuracy value were selected from the machine learning models. The results provided important information about the company's future fleet planning.

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A Comparison of Surrogate Behavioral Models for Power Amplifier Linearization under High Sparse Data

Galaviz-Aguilar

Vargas-Rosales²,

Cárdenas-Valdez³

et al. 2022

Sensors

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A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities.

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Reliable comparison for power amplifiers nonlinear behavioral modeling based on regression trees and random forest

Cited by 3 publications

References 10 publications

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

Fleet Type Planning for Private Air Transport After Covid-19

A Comparison of Surrogate Behavioral Models for Power Amplifier Linearization under High Sparse Data

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