Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Machine-learning assisted optimisation of free-parameters of a dual-input power amplifier for wideband applications

Wang, Teng, Li, Wantao, Quaglia, Roberto and Gilabert, Pere L. 2021. Machine-learning assisted optimisation of free-parameters of a dual-input power amplifier for wideband applications. Sensors 21 (8) , 2831. 10.3390/s21082831

[img] PDF - Published Version
Available under License Creative Commons Attribution.

Download (4MB)

Abstract

This paper presents an auto-tuning approach for dual-input power amplifiers using a combination of global optimisation search algorithms and adaptive linearisation in the optimisation of a multiple-input power amplifier. The objective is to exploit the extra degrees of freedom provided by dual-input topologies to enhance the power efficiency figures along wide signal bandwidths and high peak-to-average power ratio values, while being compliant with the linearity requirements. By using heuristic search global optimisation algorithms, such as the simulated annealing or the adaptive Lipschitz Optimisation, it is possible to find the best parameter configuration for PA biasing, signal calibration, and digital predistortion linearisation to help mitigating the inherent trade-off between linearity and power efficiency. Experimental results using a load-modulated balanced amplifier as device-under-test showed that after properly tuning the selected free-parameters it was possible to maximise the power efficiency when considering long-term evolution signals with different bandwidths. For example, a carrier aggregated a long-term evolution signal with up to 200 MHz instantaneous bandwidth and a peak-to-average power ratio greater than 10 dB, and was amplified with a mean output power around 33 dBm and 22.2% of mean power efficiency while meeting the in-band (error vector magnitude lower than 1%) and out-of-band (adjacent channel leakage ratio lower than −45 dBc) linearity requirements.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: MDPI
ISSN: 1424-8220
Date of First Compliant Deposit: 30 April 2021
Date of Acceptance: 15 April 2021
Last Modified: 30 Apr 2021 14:15
URI: http://orca-mwe.cf.ac.uk/id/eprint/140876

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics