Campus del Baix Llobregat
 
Projectes oferts

Projectes matriculats

Tribunals i dates de lectura

Projectes llegits
(2019-2)

DG ENG AERO/SIS TEL

DG ENG AERO/TELEMÀT

DG ENG SISTE/TELEMÀT

ENG TELEC 2NCICLE 01

ET AERO/ETT SIST 05

ET AERONÀUTICA 03

ETT SIST/ ET AERO 05

ETT SIST TELEC 00

ETT SIST TELEC 91

ETT TELEMÀTICA 00

GR ENG AERONAVEGACIÓ

GR ENG AEROPORTS

GR ENG SIS TELECOMUN

GR ENG SIST AEROESP

GR ENG TELEMÀTICA

MU AEROSPACE S&T 09

MU DRONS

MU MASTEAM 2009

MU MASTEAM 2015

Cerca projectes

Calendari TFG de dipòsit i lectura

Documentació

Web UPC


 

Projecte llegit

Títol: Linealización de un Amplificador Balanceado con Modulación de Carga Mediante un Predistorsionador Digital Basado en Redes Neuronales para Comunicaciones en Vehículos Aereos no Tripulados

Estudiants que han llegit aquest projecte:

Director: GILABERT PINAL, Pere Lluís

Departament: TSC

Títol: Linealización de un Amplificador Balanceado con Modulación de Carga Mediante un Predistorsionador Digital Basado en Redes Neuronales para Comunicaciones en Vehículos Aereos no Tripulados

Data inici oferta: 06-02-2020     Data finalització oferta: 06-10-2020


Estudis d'assignació del projecte:
    DG ENG AERO/SIS TEL
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
Amplificador de potencia, LMBA, predistorsión, redes neuronales
 
Descripció del contingut i pla d'activitats:
In wireless and wired communications, the PA is a critical subsystem in the transmitter chain. Not only because it is one of the most power hungry devices that accounts for most of the direct current (DC) power consumed in macro base stations, but also because it is the main source of nonlinear distortion in the transmitter. Amplitude and phase modulated communications signals presenting high peak-to-average power ratio (PAPR) have a negative impact in the transmitter's power efficiency, because the PA has to be operated at high power back-off levels to avoid introducing nonlinear distortion.

PA system level linearizers, such as digital predistortion (DPD), extend the linear range of PAs. Properly combined with crest factor reduction (CFR) techniques, DPD allows PAs to be driven harder into compression while meeting linearity. DPD linearization can overcome or at least mitigate the efficiency versus linearity trade-off in PAs. However, the resulting power efficiency achieved with linearization techniques applied to PAs operating as controlled current sources (e.g. class A, B, AB) is limited. To avoid wasting excessive power resources when handling high PAPR signals, either the operating conditions of a current source mode PA could be forced to follow its envelope, or switched mode amplifying classes could be properly introduced. Among the set of techniques aimed to dynamic bias or load adaptation, envelope tracking (ET) PAs, Doherty PAs, load-modulated balanced amplifiers (LMBAs) and LINC or outphasing PAs are the most widely proposed in literature.

Besides, these high efficient topologies demand specific signal processing of the main and control or auxiliary signals (e.g., proper shaping, filtering and time-alignment among signals) and the use of linearization techniques to guarantee the linearity levels specified in the communications standards. On the other hand, in order to get the best linearity vs. linearity compromise several degrees of freedom (PA biasing, shape of the auxiliary signals, DPD parameters) need to be tuned.

In many applications such as complex system design or tuning hyperparameters in machine learning algorithms, the goal is to optimize the output value of an unknown function with as few evaluations as possible. Evaluating the performance of a single set of parameters often requires numerical simulations or cross-validations with significant computational cost and the operational constraints impose a sequential exploration of the solution space with small samples. This problem is often referred to as global optimization. There is a large number of algorithms based on heuristic searches which have been introduced in order to address this problem, such as genetic algorithms, simulated annealing, model-based methods, adaLIPO or Bayesian optimization, among others.

Therefore, in such context, the main objective of this project will consist in designing an auto-tuning method for dual-input PAs, such as outphasing PAs or LMBAs, by using machine-learning-based optimization methods. In particular, apply global optimization techniques to find the best configuration for PA biasing, signal calibration and digital predistortion linearization that guarantees the linearity specifications (in terms of NMSE and ACLR) and maximizes power efficiency of dual-input PAs, when considering wide-bandwidth signals (hundreds of MHz) presenting PAPRs greater than 10 dB.
 
Overview (resum en anglès):
This final degree project focuses on the analysis and linearization of a load modulated balanced amplifier (LMBA), designed to operate with wide bandwidth signals without degrading the amplifier's efficiency.

This type of amplifier can be used in unmanned aerial vehicles that require a high data transmission rate, where it is critical to maintain high levels of efficiency to minimize consumption and maximize flight autonomy, as well as in base stations to be used with the new 5G standard.

In this project the parameters involved in the LMBA (the phase shift between the two signals that control the amplifier and the relationship between these two amplitudes) and its effects on the linearity and efficiency are analyzed. In addition, a number of steps are proposed to follow for proper settings and get the most out of this amplifier.

Finally, the linearization of the amplifier is performed with a digital predistorter based on an artificial neural network made up of several hidden layers, and these results are compared to the results obtained with the GMP (Generalized Memory Polynomial) method for different LTE signals.


Data de generació 26/01/2021