Digital predistortion based on cascaded behavioral models and artificial neural networks to enhance the linearity in 5G non-terrestrial networks
Tutor/a - Director/a
Estudiant
Criado Simón, Raúl
Tipus de document
Treball Final de Grau
Data
2024
rights
Accés obert
Editorial
Universitat Politècnica de Catalunya
UPCommons
Resum
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).
Entitat col·laboradora
CSC Components and Systems for Communications Research Group
