Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers
Digital predistortion (DPD) is essential for improving the efficiency and linearity of power amplifiers (PAs), particularly in radio frequency communication systems. We propose a wavelet decomposition prediction (WDP) framework that better adapts to the highly nonlinear characteristics of PAs. In th...
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MDPI AG
2025-03-01
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| author | Shaocheng Peng Jing You |
| author_facet | Shaocheng Peng Jing You |
| author_sort | Shaocheng Peng |
| collection | DOAJ |
| description | Digital predistortion (DPD) is essential for improving the efficiency and linearity of power amplifiers (PAs), particularly in radio frequency communication systems. We propose a wavelet decomposition prediction (WDP) framework that better adapts to the highly nonlinear characteristics of PAs. In this framework, the input data are first decomposed using wavelet transformation, allowing for a more effective representation of nonlinear features. Next, a nonlinear modeling process is conducted on the PA to capture its distortion characteristics. Once the nonlinear model is trained, it is frozen to preserve its learned features. Based on this frozen nonlinear model, DPD is then applied to the PA to compensate for nonlinear distortions. Experimental results demonstrate the effectiveness of our proposed method, achieving the best ACPR and EVM performance on the OpenDPD dataset. |
| format | Article |
| id | doaj-art-af44271c8bcd4cafbcdaa46883c930a0 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-af44271c8bcd4cafbcdaa46883c930a02025-08-20T03:08:44ZengMDPI AGApplied Sciences2076-34172025-03-01157359910.3390/app15073599Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power AmplifiersShaocheng Peng0Jing You1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaDigital predistortion (DPD) is essential for improving the efficiency and linearity of power amplifiers (PAs), particularly in radio frequency communication systems. We propose a wavelet decomposition prediction (WDP) framework that better adapts to the highly nonlinear characteristics of PAs. In this framework, the input data are first decomposed using wavelet transformation, allowing for a more effective representation of nonlinear features. Next, a nonlinear modeling process is conducted on the PA to capture its distortion characteristics. Once the nonlinear model is trained, it is frozen to preserve its learned features. Based on this frozen nonlinear model, DPD is then applied to the PA to compensate for nonlinear distortions. Experimental results demonstrate the effectiveness of our proposed method, achieving the best ACPR and EVM performance on the OpenDPD dataset.https://www.mdpi.com/2076-3417/15/7/3599digital predistortionpower amplifierswavelet decomposition prediction |
| spellingShingle | Shaocheng Peng Jing You Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers Applied Sciences digital predistortion power amplifiers wavelet decomposition prediction |
| title | Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers |
| title_full | Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers |
| title_fullStr | Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers |
| title_full_unstemmed | Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers |
| title_short | Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers |
| title_sort | wavelet decomposition prediction for digital predistortion of wideband power amplifiers |
| topic | digital predistortion power amplifiers wavelet decomposition prediction |
| url | https://www.mdpi.com/2076-3417/15/7/3599 |
| work_keys_str_mv | AT shaochengpeng waveletdecompositionpredictionfordigitalpredistortionofwidebandpoweramplifiers AT jingyou waveletdecompositionpredictionfordigitalpredistortionofwidebandpoweramplifiers |