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Dynamic behavioral modeling of RF power amplifiers based on decomposed piecewise machine learning technique
International Journal of Microwave and Wireless Technologies ( IF 1.4 ) Pub Date : 2020-08-28 , DOI: 10.1017/s1759078720001208
Jialin Cai , Justin B. King , Chao Yu , Baicao Pan , Lingling Sun , Jun Liu

Multi-device radio frequency power amplifiers (PAs) often exhibit strongly non-linear behavior in combination with long-term memory effects, leading to an extremely challenging model development cycle. This paper presents a new, dynamic, behavioral modeling technique, based on a combination of the real-valued decomposed piecewise method and concepts from the field of machine learning. The underlying theory of the proposed modeling technique is provided, along with a detailed modeling procedure. Experimental results show that the proposed decomposed piecewise support vector regression (SVR) model leads to significant performance improvements when compared with standard SVR models for both single transistor and multi-transistor PAs. Different model thresholds are used to test the proposed model performance for both PA types. For the single-transistor PA, modeled using only one partition, an approximately 10 dB normalized mean square error (NMSE) reduction is seen when compared with the standard SVR model. For the same PA, when utilizing two partitions, the reduction improves to 14 dB. When applied to a multi-device Doherty PA, the NMSE between model and measurement data is −50 dB, representing more than 10 dB improvement compared with the standard SVR model.

中文翻译:

基于分解分段机器学习技术的射频功率放大器动态行为建模

多设备射频功率放大器 (PA) 通常表现出强烈的非线性行为以及长期记忆效应,导致模型开发周期极具挑战性。本文提出了一种新的动态行为建模技术,该技术基于实值分解分段方法和机器学习领域的概念的组合。提供了所提出的建模技术的基本理论,以及详细的建模过程。实验结果表明,与单晶体管和多晶体管 PA 的标准 SVR 模型相比,所提出的分解分段支持向量回归 (SVR) 模型可显着提高性能。不同的模型阈值用于测试两种 PA 类型的建议模型性能。对于仅使用一个分区建模的单晶体管 PA,与标准 SVR 模型相比,可以看到大约 10 dB 归一化均方误差 (NMSE) 降低。对于相同的 PA,当使用两个分区时,降低幅度提高到 14 dB。当应用于多设备 Doherty PA 时,模型和测量数据之间的 NMSE 为 -50 dB,与标准 SVR 模型相比提高了 10 dB 以上。
更新日期:2020-08-28
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