Abstract
A power amplifier (PA) is the core module of the wireless communication system. The change of its specification directly affects the system's performance and may even lead to system failure. Furthermore, change in the PA specification is closely related to changes in temperature. To study the influence of PA specification change on the system, we used a support vector machine (SVM) to model the temperature characteristics of PA. For SVM modeling, the question of how much experimental data should be used for modeling to meet the requirements is a constant problem. To address this issue, we investigate the effect of different amounts of training data on the modeling of SVM models. The results show that only 75% of the experimental data needs to be used in the modeling process to satisfy the modeling requirements of the SVM model. The number of measurement points required in the PA specification degradation experiment can be reduced by 25%. The results of this paper serve as a guide for planning the number of experimental measurement points and reducing the measurement cost and measurement time.