A new digital predictive predistorter for behavioral power amplifier linearization
This letter presents a new digital adaptive predistorter (PD) for power amplifier (PA) linearization based on a nonlinear auto-regressive moving average (NARMA) structure. The distinctive characteristic of this PD is its straightforward deduction from the NARMA PA model, without the need of using an...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/2027 |
| Acceso en línea: | https://hdl.handle.net/2117/2027 |
| Access Level: | acceso abierto |
| Palabra clave: | Power amplifiers Power Amplifier Linearization Predictive Digital Predistorter Electrònica de potència Amplificadors -- Alta freqüència Àrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència |
| Sumario: | This letter presents a new digital adaptive predistorter (PD) for power amplifier (PA) linearization based on a nonlinear auto-regressive moving average (NARMA) structure. The distinctive characteristic of this PD is its straightforward deduction from the NARMA PA model, without the need of using an indirect learningapproachto identify the PD function.The PD itself presents a NARMA structure, and hence it can be quickly implemented by means of lookup tables. Single and multicarrier modulated signals collected from a three-stage LDMOS class AB PA, with a maximum output power of 48-dBm CW have been used to validate the linearity performance of this new predictive predistorter. |
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