Automatic Piecewise Extreme Learning Machine-Based Model for S-Parameters of RF Power Amplifier

Wang, Lulu; Zhou, Shaohua; Fang, Wenrao; Huang, Wenzhu; Yang, Zhiqiang; Fu, Chao; Liu, Changkun

doi:10.3390/mi14040840

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…As an essential part of wireless communication systems, the performance of radio-frequency (RF) power amplifiers (PA) play an important role in wireless communication systems [ 1 ]. Modeling of RF PA is crucial for wireless communication [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Wang,

Zhou

2023

Micromachines

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Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA’s crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (−40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.

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Section: Introductionmentioning

confidence: 99%

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

Wang,

Zhou

2023

Micromachines

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“…5,6 S-parameters are specifications for characterizing microwave devices, and RF PA is no exception. 7 Therefore, modeling S-parameters of RF PA can effectively characterize and describe its performance.…”

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PSO‐GA‐SVR model for S‐parameters of radio‐frequency power amplifier under different temperatures

Wang,

Zhou

2023

Int J Numerical Modelling

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Support vector machine (SVR) has been introduced into the modeling of S‐parameters in radio‐frequency (RF) power amplifiers (PA). The modeling accuracy and speed of SVR are primarily affected by the penalty parameter c and the kernel function coefficient γ. Using the traditional grid search technique to determine these two parameters is time‐consuming and labor‐intensive, and ensuring the model's accuracy is not easy. This article proposes an S‐parameters modeling method based on PSO‐GA‐SVR to improve the SVR's modeling accuracy and speed. The model mainly focuses on particle swarm optimization (PSO) and combines selection, crossover, and mutation operations in genetic algorithms (GA). The fitness values are arranged from small to large in each iteration process, and the first 1/3 are selected for crossover and mutation. Then, the resulting new particle swarms are introduced into the original particle swarm population for searching. On the one hand, PSO‐GA extends the population size and reduces the possibility of falling into local optimization. On the other hand, due to population size expansion, the number of iteration rounds is reduced, and the modeling speed is also increased. The experimental results show that compared to SVR, GA‐SVR, and PSO‐SVR, the proposed PSO‐GA‐SVR can improve the modeling accuracy by more than one magnitude or more while also increasing modeling speed by one magnitude or more. Furthermore, compared with the classical machine learning algorithms such as gradient boosting, random forest, and gcForset, the proposed PSO‐GA‐SVR improves the modeling accuracy by one order of magnitude and the modeling speed by two orders of magnitude more than gradient boosting, random forest, and improves the modeling speed by one order of magnitude more than gcForset.

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Automatic Piecewise Extreme Learning Machine-Based Model for S-Parameters of RF Power Amplifier

Cited by 2 publications

References 22 publications

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures

PSO‐GA‐SVR model for S‐parameters of radio‐frequency power amplifier under different temperatures

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