Projecte matriculat
Títol: Digital predistortion based on cascaded behavioral models and artificial neural networks to enhance the linearity in 5G non-terrestrial networks
Director/a: GILABERT PINAL, PERE LLUÍS
Departament: TSC
Títol: Digital predistortion based on cascaded behavioral models and artificial neural networks to enhance the linearity in 5G non-terrestrial networks
Data inici oferta: 30-01-2024 Data finalització oferta: 30-09-2024
Estudis d'assignació del projecte:
DG ENG AERO/SIS TEL
| Tipus: Individual | |
| Lloc de realització: EETAC | |
| Segon director/a (UPC): LI, WANTAO | |
| Paraules clau: | |
| load-modulated balanced amplifier, digital predistortion, 5G non-terrestrial networks | |
| Descripció del contingut i pla d'activitats: | |
| Over the last years, 5G technology has been playing an increasingly important role in the aeronautical and aerospace sectors. 5G NTN (Non-Terrestrial Networks) consists of a network with the objective of complement the conventional terrestrial 5G connectivity by means of components such as GEO, MEO, LEO satellite constellations, High Altitude Platform Systems (HAPS), Low Altitude Platform Systems (LAPS) and air-to-ground (A2G)
networks. Some of these links can range from tens to hundreds of kilometres. To fulfil this long range communications, it is clearly seen that the transmitted signal power should be large enough to overcome link losses. For that reason, the amplification stage plays a significant role. Highly efficient amplification architectures such as the load-modulated balance amplifier (LMBA), make use of the dynamic load modulation principle to keep the power efficiency figures high when the power amplifier is operated high peak-to-average power (PAPR) signals, such is the case of 5G new radio ones. With the objective of achieving high amplification as well as keeping fairly good efficiency some signal linearization should be performed using digital predistortion (DPD) techniques. This project delves into a comparison among some of the most commonly used DPD linearization behavioral models in terms of robustness in front of different signal parameters such as bandwidth or input power. These models are single stage polynomial-based models, N-stage cascaded models and artificial neural networks (ANN). |
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| Orientació a l'estudiant: | |
| Requereix activitats hardware: Si | |
| Requereix activitats software: Sí Sistema operatiu: Disc (Gb): | |
| Horari d'atenció a estudiants per a l'assignació de projecte: |
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