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用于宽带应用的双输入功率放大器自由参数的机器学习辅助优化

Machine-Learning Assisted Optimisation of Free-Parameters of a Dual-Input Power Amplifier for Wideband Applications.

作者信息

Wang Teng, Li Wantao, Quaglia Roberto, Gilabert Pere L

机构信息

Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, 08034 Barcelona, Spain.

Centre for High Frequency Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK.

出版信息

Sensors (Basel). 2021 Apr 17;21(8):2831. doi: 10.3390/s21082831.

DOI:10.3390/s21082831
PMID:33920523
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8073864/
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.

摘要

本文提出了一种双输入功率放大器的自动调谐方法,该方法在多输入功率放大器的优化中结合了全局优化搜索算法和自适应线性化。目的是利用双输入拓扑结构提供的额外自由度,在宽信号带宽和高峰均功率比的情况下提高功率效率指标,同时满足线性度要求。通过使用启发式搜索全局优化算法,如模拟退火算法或自适应李普希茨优化算法,可以找到功率放大器偏置、信号校准和数字预失真线性化的最佳参数配置,以帮助减轻线性度和功率效率之间固有的权衡。使用负载调制平衡放大器作为被测器件的实验结果表明,在对选定的自由参数进行适当调谐后,在考虑具有不同带宽的长期演进信号时,可以实现功率效率的最大化。例如,一个载波聚合了一个瞬时带宽高达200 MHz且峰均功率比大于10 dB的长期演进信号,并在满足带内(误差矢量幅度低于1%)和带外(邻道泄漏比低于-45 dBc)线性度要求的情况下,以平均输出功率约33 dBm和平均功率效率22.2%进行放大。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/80e1581c5131/sensors-21-02831-g014.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/188e5bd9fc05/sensors-21-02831-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/daa0351262d9/sensors-21-02831-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/0152c63b5671/sensors-21-02831-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/d4d80098c6fa/sensors-21-02831-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/da706303caa0/sensors-21-02831-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/72b6591673ca/sensors-21-02831-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/80e1581c5131/sensors-21-02831-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/b81be0841768/sensors-21-02831-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/188e5bd9fc05/sensors-21-02831-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/daa0351262d9/sensors-21-02831-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/0152c63b5671/sensors-21-02831-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/d4d80098c6fa/sensors-21-02831-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/da706303caa0/sensors-21-02831-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1609/8073864/80e1581c5131/sensors-21-02831-g014.jpg

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Optimization by simulated annealing.模拟退火优化。
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