Artificial techniques applied to the improvement of the previous signals in the power amplifiers
A rapid evolution in electronic systems has been experienced in recent years, and one of the fields where this development has been notorious is the telecommunication systems in which users demand more and better services and with higher data transfer speeds. This has generated the need to develop n...
- Autores:
-
amelec, viloria
Lizardo Zelaya, Nelson Alberto
Mercado Caruso, Nohora Nubia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7693
- Acceso en línea:
- https://hdl.handle.net/11323/7693
https://doi.org/10.1016/j.procs.2020.07.091
https://repositorio.cuc.edu.co/
- Palabra clave:
- Comparative study
Neural networks
Digital pre-distortion
RF amplifiers
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| title |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| spellingShingle |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers Comparative study Neural networks Digital pre-distortion RF amplifiers |
| title_short |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| title_full |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| title_fullStr |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| title_full_unstemmed |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| title_sort |
Artificial techniques applied to the improvement of the previous signals in the power amplifiers |
| dc.creator.fl_str_mv |
amelec, viloria Lizardo Zelaya, Nelson Alberto Mercado Caruso, Nohora Nubia |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Lizardo Zelaya, Nelson Alberto Mercado Caruso, Nohora Nubia |
| dc.subject.spa.fl_str_mv |
Comparative study Neural networks Digital pre-distortion RF amplifiers |
| topic |
Comparative study Neural networks Digital pre-distortion RF amplifiers |
| description |
A rapid evolution in electronic systems has been experienced in recent years, and one of the fields where this development has been notorious is the telecommunication systems in which users demand more and better services and with higher data transfer speeds. This has generated the need to develop new devices, algorithms and systems that manage to satisfy the requirements demanded y new technologies. An example of the above is the front-end of telecommunication systems. Systems need to be more efficient, but some elements of the systems, as the power amplifier, present nonlinearity when operating in its most efficient region, causing that it has to make a commitment between efficiency and linearity. This paper presents a comparison of different artificial neural network architectures, as a behavioral modeling method, to perform digital predistortion of power amplifiers. |
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2020 |
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2020 |
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2021-01-15T14:14:56Z |
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2021-01-15T14:14:56Z |
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Artículo de revista |
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https://doi.org/10.1016/j.procs.2020.07.091 |
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Corporación Universidad de la Costa |
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eng |
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eng |
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[1] Liu, T., Ye, Y., Yin, S., Chen, H., Xu, G., Lu, Y., & Chen, Y. (2019, May). Digital Predistortion Linearization with Deep Neural Networks for 5G Power Amplifiers. In 2019 European Microwave Conference in Central Europe (EuMCE) (pp. 216-219). IEEE. [2] Phartiyal, D., & Rawat, M. (2019, February). LSTM-Deep Neural Networks based Predistortion Linearizer for High Power Amplifiers. In 2019 National Conference on Communications (NCC) (pp. 1-5). IEEE. [3] Viloria, A., Hernández Palma, H., Gamboa Suarez, R., Niebles Núẽz, W., & Solórzano Movilla, J. (2020). Intelligent Model for Electric Power Management: Patterns. In Journal of Physics: Conference Series (Vol. 1432). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1432/1/012032. [4] Sun, J., Wang, J., Guo, L., Yang, J., & Gui, G. (2020). Adaptive deep learning aided digital predistorter considering dynamic envelope. IEEE Transactions on Vehicular Technology. [5] Tripathi, G. C., Rawat, M., & Rawat, K. (2019, October). Swish Activation Based Deep Neural Network Predistorter for RF-PA. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1239-1242). IEEE. [6] Tripathi, G. C., Rawat, M., & Rawat, K. (2019, October). Swish Activation Based Deep Neural Network Predistorter for RF-PA. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1239-1242). IEEE. [7] Cioba, A., Chua, A., Shiu, D. S., Kuo, T. H., & Peng, C. S. (2020). Efficient attention guided 5G power amplifier digital predistortion. arXiv preprint arXiv:2003.13361. [8] Rawat, M., Rawat, K., & Ghannouchi, F. M. (2009). Adaptive digital predistortion of wireless power amplifiers/transmitters using dynamic real-valued focused time-delay line neural networks. IEEE Transactions on Microwave Theory and Techniques, 58(1), 95-104. [9] Isaksson, M. (2007). Radio Frequency Power Amplifiers: Behavioral Modeling, Parameter-Reduction, and Digital Predistortion (Doctoral dissertation, Royal Institute of Technology). [10] Xiang, T. and Wang, G. Doherty power amplifier with feedforward linearization, 2009 Asia Pacific Microwave Conference, Singapore, 2009, pp. 1621-1624 [11] Watkins, B. E., North, R., & Tummala, M. (1995, November). Neural network based adaptive predistortion for the linearization of nonlinear RF amplifiers. In Proceedings of MILCOM'95 (Vol. 1, pp. 145-149). IEEE. [12] Watkins, B. E., & North, R. (1996, October). Predistortion of nonlinear amplifiers using neural networks. In Proceedings of MILCOM'96 IEEE Military Communications Conference (Vol. 1, pp. 316-320). IEEE. [13] Viloria, A., Senior Naveda, A., Hernández Palma, H., Niebles Núẽz, W., & Niebles Núẽz, L. (2020). Electrical Consumption Patterns through Machine Learning. In Journal of Physics: Conference Series (Vol. 1432). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1432/1/012093. |
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amelec, viloriaLizardo Zelaya, Nelson AlbertoMercado Caruso, Nohora Nubia2021-01-15T14:14:56Z2021-01-15T14:14:56Z20201877-0509https://hdl.handle.net/11323/7693https://doi.org/10.1016/j.procs.2020.07.091Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A rapid evolution in electronic systems has been experienced in recent years, and one of the fields where this development has been notorious is the telecommunication systems in which users demand more and better services and with higher data transfer speeds. This has generated the need to develop new devices, algorithms and systems that manage to satisfy the requirements demanded y new technologies. An example of the above is the front-end of telecommunication systems. Systems need to be more efficient, but some elements of the systems, as the power amplifier, present nonlinearity when operating in its most efficient region, causing that it has to make a commitment between efficiency and linearity. This paper presents a comparison of different artificial neural network architectures, as a behavioral modeling method, to perform digital predistortion of power amplifiers.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Lizardo Zelaya, Nelson AlbertoMercado Caruso, Nohora Nubia-will be generated-orcid-0000-0001-9261-8331-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317919Comparative studyNeural networksDigital pre-distortionRF amplifiersArtificial techniques applied to the improvement of the previous signals in the power amplifiersArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Liu, T., Ye, Y., Yin, S., Chen, H., Xu, G., Lu, Y., & Chen, Y. (2019, May). Digital Predistortion Linearization with Deep Neural Networks for 5G Power Amplifiers. In 2019 European Microwave Conference in Central Europe (EuMCE) (pp. 216-219). IEEE.[2] Phartiyal, D., & Rawat, M. (2019, February). LSTM-Deep Neural Networks based Predistortion Linearizer for High Power Amplifiers. In 2019 National Conference on Communications (NCC) (pp. 1-5). IEEE.[3] Viloria, A., Hernández Palma, H., Gamboa Suarez, R., Niebles Núẽz, W., & Solórzano Movilla, J. (2020). Intelligent Model for Electric Power Management: Patterns. In Journal of Physics: Conference Series (Vol. 1432). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1432/1/012032.[4] Sun, J., Wang, J., Guo, L., Yang, J., & Gui, G. (2020). Adaptive deep learning aided digital predistorter considering dynamic envelope. IEEE Transactions on Vehicular Technology.[5] Tripathi, G. C., Rawat, M., & Rawat, K. (2019, October). Swish Activation Based Deep Neural Network Predistorter for RF-PA. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1239-1242). IEEE.[6] Tripathi, G. C., Rawat, M., & Rawat, K. (2019, October). Swish Activation Based Deep Neural Network Predistorter for RF-PA. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1239-1242). IEEE.[7] Cioba, A., Chua, A., Shiu, D. S., Kuo, T. H., & Peng, C. S. (2020). Efficient attention guided 5G power amplifier digital predistortion. arXiv preprint arXiv:2003.13361.[8] Rawat, M., Rawat, K., & Ghannouchi, F. M. (2009). Adaptive digital predistortion of wireless power amplifiers/transmitters using dynamic real-valued focused time-delay line neural networks. IEEE Transactions on Microwave Theory and Techniques, 58(1), 95-104.[9] Isaksson, M. (2007). Radio Frequency Power Amplifiers: Behavioral Modeling, Parameter-Reduction, and Digital Predistortion (Doctoral dissertation, Royal Institute of Technology).[10] Xiang, T. and Wang, G. Doherty power amplifier with feedforward linearization, 2009 Asia Pacific Microwave Conference, Singapore, 2009, pp. 1621-1624[11] Watkins, B. E., North, R., & Tummala, M. (1995, November). Neural network based adaptive predistortion for the linearization of nonlinear RF amplifiers. In Proceedings of MILCOM'95 (Vol. 1, pp. 145-149). IEEE.[12] Watkins, B. E., & North, R. (1996, October). Predistortion of nonlinear amplifiers using neural networks. In Proceedings of MILCOM'96 IEEE Military Communications Conference (Vol. 1, pp. 316-320). IEEE.[13] Viloria, A., Senior Naveda, A., Hernández Palma, H., Niebles Núẽz, W., & Niebles Núẽz, L. (2020). Electrical Consumption Patterns through Machine Learning. In Journal of Physics: Conference Series (Vol. 1432). 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