Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks
In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifi...
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/21451 |
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| _version_ | 1864513438410080256 |
|---|---|
| author | Khawam, Yahya Bader |
| author2 | Hammi, Oualid Albasha, Lutfi Mir, Hasan |
| author2_role | author author author |
| author_facet | Khawam, Yahya Bader Hammi, Oualid Albasha, Lutfi Mir, Hasan |
| author_role | author |
| dc.creator.none.fl_str_mv | Khawam, Yahya Bader Hammi, Oualid Albasha, Lutfi Mir, Hasan |
| dc.date.none.fl_str_mv | 2020 2021-04-27T07:28:53Z 2021-04-27T07:28:53Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Y. Khawam, O. Hammi, L. Albasha and H. Mir, "Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks," in IEEE Access, vol. 8, pp. 202707-202715, 2020, doi: 10.1109/ACCESS.2020.3036186. 2169-3536 http://hdl.handle.net/11073/21451 10.1109/ACCESS.2020.3036186 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://ieeexplore.ieee.org/document/9248991 |
| dc.subject.none.fl_str_mv | AM-AM AM-PM Digital pre-distortion Doherty power amplifier Linearization Memory effect Polynomial model Deep neural network Bidirectional LSTM Convolutional neural networks |
| dc.title.none.fl_str_mv | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | In this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as - 45:3dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as - 46:1dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data. |
| format | article |
| id | aus_b1aa0dfb8b9d6c3edf0d15f169b587c3 |
| identifier_str_mv | Y. Khawam, O. Hammi, L. Albasha and H. Mir, "Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks," in IEEE Access, vol. 8, pp. 202707-202715, 2020, doi: 10.1109/ACCESS.2020.3036186. 2169-3536 10.1109/ACCESS.2020.3036186 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21451 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural NetworksKhawam, Yahya BaderHammi, OualidAlbasha, LutfiMir, HasanAM-AMAM-PMDigital pre-distortionDoherty power amplifierLinearizationMemory effectPolynomial modelDeep neural networkBidirectional LSTMConvolutional neural networksIn this paper, novel Doherty Power Amplifier (DPA) models are presented. The motivation behind the proposed models is to accurately predict static nonlinearities in the compression regions of the carrier and peaking amplifiers. DPAs suffer from a nonlinearity that originates from the carrier amplifier, and a second more pronounced nonlinearity generated at the full compression region following the turn-on of the peaking amplifier. Moreover, these distortions are often observed at different input power levels depending on whether the AM-AM or the AM-PM characteristic is considered. Therefore, the proposed static model is based on independent modeling of the memoryless gain in the polar domain. The static model of the memoryless AM-AM and AM-PM characteristics is augmented with either memory polynomials or deep neural network functions for memory effects modeling. The methodology of building the proposed models and the achieved results are discussed in this paper. The MP based proposed model achieves an NMSE as low as - 45:3dB with only 78 model parameters, while the DNN based model achieves an NMSE as low as - 46:1dB with only 156 model parameters. However, the DNN based model achieves the best model resilience to changes in the identification data.American University of SharjahIEEE2021-04-27T07:28:53Z2021-04-27T07:28:53Z2020Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfY. Khawam, O. Hammi, L. Albasha and H. Mir, "Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks," in IEEE Access, vol. 8, pp. 202707-202715, 2020, doi: 10.1109/ACCESS.2020.3036186.2169-3536http://hdl.handle.net/11073/2145110.1109/ACCESS.2020.3036186en_UShttps://ieeexplore.ieee.org/document/9248991oai:repository.aus.edu:11073/214512024-08-22T12:08:27Z |
| spellingShingle | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks Khawam, Yahya Bader AM-AM AM-PM Digital pre-distortion Doherty power amplifier Linearization Memory effect Polynomial model Deep neural network Bidirectional LSTM Convolutional neural networks |
| status_str | publishedVersion |
| title | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| title_full | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| title_fullStr | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| title_full_unstemmed | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| title_short | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| title_sort | Behavioral Modeling of GaN Doherty Power Amplifiers Using Memoryless Polar Domain Functions and Deep Neural Networks |
| topic | AM-AM AM-PM Digital pre-distortion Doherty power amplifier Linearization Memory effect Polynomial model Deep neural network Bidirectional LSTM Convolutional neural networks |
| url | http://hdl.handle.net/11073/21451 |