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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.

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper compares and discusses four\ud techniques for model order reduction based on compressed\ud sensing (CS), less relevant basis removal (LRBR), principal\ud component analysis (PCA) and partial least squares (PLS). CS\ud and PCA have already been used for reducing the order of power\ud amplifier (PA) behavioral models for digital predistortion (DPD)\ud purposes. While PLS, despite being popular in some signal\ud processing areas, to the best author’s knowledge, still has not\ud been used in the PA linearization field. Finally, the LRBR is an\ud iterative search algorithm proposed by the authors in this paper\ud for the sake of comparison. Experimental results are presented\ud and the advantages and drawbacks of each method discussed.Peer ReviewedPostprint (author's final draft

ourth-generation (4G) communication systems based on orthogonal frequency division multiplexing (OFDM) and the proposed backwards compatible fifthgeneration (5G) variants, like filter-bank multi-carrier (FBMC), are based on modulation techniques that allow significantly increased spectral efficiency and capacity in mobile radio access networks (RANs). However, the use of these modulation techniques impacts the requirements of the radio base stations, which have been traditionally the most energy consuming-element of mobile networks, accounting for up to 80% of the energy consumption of RANs [1]. Non-constant envelope-modulation techniques with high peak-to-average power ratios (PAPRs) require highly linear power amplifier (PA) amplitude and phase responses to fulfill stringent spectral mask and modulation accuracy requirements. This is often achieved with significant PA back-off, which considerably reduces PA efficiency because the PA's maximum efficiency is achieved near the saturation point. The adoption of gallium nitride (GaN) PA technologies and use of digital linearization techniques are playing a key role in building more efficient base