Chebyshev Polynomial-LSTM Model for 5G Millimeter-Wave Power Amplifier Linearization

In this letter, a behavior model, namely CP- LSTM, composed of Chebyshev polynomials (CP) and a long short-term memory (LSTM) network is built to linearize the wideband millimeter-wave power amplifier (mmW PA) in the fifth-generation (5G) mobile communication system. In order to verify the lineariza...

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Bibliographic Details
Published inIEEE microwave and wireless components letters Vol. 32; no. 6; pp. 611 - 614
Main Authors Xu, Gaoming, Yu, Huihui, Hua, Changzhou, Liu, Taijun
Format Journal Article
LanguageEnglish
Published IEEE 01.06.2022
Subjects
5G mobile communication
Chebyshev approximation
Chebyshev polynomials (CPs)
Data models
digital predistortion
long short-term memory (LSTM)
Loss measurement
power amplifier (PA)
Radio frequency
Wideband
Wireless communication
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Summary:In this letter, a behavior model, namely CP- LSTM, composed of Chebyshev polynomials (CP) and a long short-term memory (LSTM) network is built to linearize the wideband millimeter-wave power amplifier (mmW PA) in the fifth-generation (5G) mobile communication system. In order to verify the linearization performance of the CP- LSTM predistorter, a 100-MHz bandwidth 5G new radio (5G NR) signal is employed to test the 28-GHz mmW PA under the text. Experimental results show that the adjacent channel power ratio (ACPR) of the mmW PA with the CP- LSTM can be improved by 20 dB which is 5-dB better than with the LSTM and 3-dB better than with the generalized memory polynomial (GMP). Therefore, the proposed CP- LSTM model is very effective to linearize 5G mmW PAs.
ISSN:1531-1309
1558-1764
DOI:10.1109/LMWC.2022.3142423