CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures
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 param...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/14/9/1673 |
| _version_ | 1797578850666807296 |
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| author | Jiayi Wang Shaohua Zhou |
| author_facet | Jiayi Wang Shaohua Zhou |
| author_sort | Jiayi Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:27:33Z |
| format | Article |
| id | doaj.art-e7c9d231b6f44bf682172398d2f5387e |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T22:27:33Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-e7c9d231b6f44bf682172398d2f5387e2023-11-19T11:59:02ZengMDPI AGMicromachines2072-666X2023-08-01149167310.3390/mi14091673CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different TemperaturesJiayi Wang0Shaohua Zhou1School of Micro-Nano Electronics, Zhejiang University, Hangzhou 310058, ChinaZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 310058, ChinaMachine 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.https://www.mdpi.com/2072-666X/14/9/1673XGBoostcuckoo searchgenetic algorithmmodelingpower amplifier |
| spellingShingle | Jiayi Wang Shaohua Zhou CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures Micromachines XGBoost cuckoo search genetic algorithm modeling power amplifier |
| title | CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures |
| title_full | CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures |
| title_fullStr | CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures |
| title_full_unstemmed | CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures |
| title_short | CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures |
| title_sort | cs ga xgboost based model for a radio frequency power amplifier under different temperatures |
| topic | XGBoost cuckoo search genetic algorithm modeling power amplifier |
| url | https://www.mdpi.com/2072-666X/14/9/1673 |
| work_keys_str_mv | AT jiayiwang csgaxgboostbasedmodelforaradiofrequencypoweramplifierunderdifferenttemperatures AT shaohuazhou csgaxgboostbasedmodelforaradiofrequencypoweramplifierunderdifferenttemperatures |