This work presents insights on the application of the Bayesian information criterion (BIC) to fix the optimum number of coefficients in the Volterra series applied to the modeling and linearization of power amplifiers (PAs). The BIC is transformed from a rule to be applied after selection techniques to a stopping criterion, which enables the halting of the algorithm when a condition is reached. This study reveals that the BIC is equivalent to allow a certain identification normalized mean square error (NMSE) decrease after the inclusion of a model component. Experimental results of the digital predistortion of a class J PA are provided, demonstrating the proposal applicability in the attaining of the optimum number of coefficients. A comparison is made between the results obtained when the stopping rule is applied to the hill climbing (HC) and the doubly orthogonal matching pursuit (DOMP) algorithms.

中文翻译：

---

关于稀疏数字预失真器的最佳系数数：贝叶斯方法

这项工作提出了关于应用贝叶斯信息准则 (BIC) 来确定应用于功率放大器 (PA) 建模和线性化的 Volterra 系列中系数的最佳数量的见解。BIC 从在选择技术后应用的规则转换为停止标准，当达到条件时，该标准能够停止算法。该研究表明，BIC 等效于允许在包含模型组件后降低一定的识别归一化均方误差 (NMSE)。提供了 J 类 PA 数字预失真的实验结果，证明了该提议在获得最佳系数数量方面的适用性。