Linear-Decomposition Digital Predistortion of Power Amplifiers for 5G Ultrabroadband Applications
| Autor: | Qianyun Lu, Hang Yin, Wei Hong, Chao Yu, Jialin Cai, Xiao-Wei Zhu, Jixin Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Radiation
Computer science Amplifier Bandwidth (signal processing) 020206 networking & telecommunications 02 engineering and technology Condensed Matter Physics Predistortion Sampling (signal processing) Linearization Broadband 0202 electrical engineering electronic engineering information engineering Electronic engineering Oversampling Waveform Electrical and Electronic Engineering |
| Zdroj: | IEEE Transactions on Microwave Theory and Techniques. 68:2833-2844 |
| ISSN: | 1557-9670 0018-9480 |
| DOI: | 10.1109/tmtt.2020.2975637 |
| Popis: | This article proposes a novel linear-decomposition digital predistortion (LD-DPD) for the linearization of power amplifiers (PAs) in ultra broadband applications. LD-DPD is able to perform excellently with low complexity at the oversampling rate of only $1.5\times $ . It combines leading terms with a linear decomposition of cross-terms, which can reduce the spectrum aliasing even when sampling rates are insufficient. The model complexity of the proposed model is quantified by floating-point operations. Experiments on broadband PAs at both sub-6-GHz and millimeter-wave frequencies have provided effective validations of the proposed model by employing the 5G New Radio (NR) waveforms. Particularly, the linearization of a signal with modulated bandwidth (BW) 800 MHz at 28 GHz was demonstrated. LD-DPD achieved a normalized root-mean-square error (NRMSE) of 3.34% for continuous 800-MHz modulated signal at the sampling rate of only 1.2 GHz. Compared with the already published DPDs, the proposed method is featured by high accuracy with limited available sampling rates to significantly reduce the system BW requirements. Also, it presents much lower complexity that will reduce expenses on hardware, which is very suitable for 5G ultra broadband applications. |
| Databáze: | OpenAIRE |
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