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Projecte llegit

Títol: Digital predistortion based on cascaded behavioral models and artificial neural networks to enhance the linearity in 5G non-terrestrial networks

Estudiants que han llegit aquest projecte:

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).
 
Overview (resum en anglès):
Over the last years, 5G technology has been playing an increasingly important role in the aeronautical and aerospace sectors. 5G Non-Terrestrial Networks (NTN) consist of a set of elements whose objective is to complement 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 these longrange 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 operating high peak-to-average power (PAPR) signals, such as 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 trending DPD linearization behavioral models in terms of performance and implementation complexity. These models are the single stage polynomial-based models, the N-stage cascaded polynomial-based models, and those based in artificial neural networks (ANN).

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