一种适用于并发双频段功放的新型简化数字预失真模型
发布时间：2019-02-23 09:26
【摘要】：现今支持并发双频段的功率放大器(Power Amplifier,PA)相继出现,而传统的双频段预失真模型复杂度较高,实现难度大。本文针对并发双频段功率放大器的特殊非线性特性,提出了一种新型简化双频段数字预失真模型(Modified Odd Dual-Band Digital Predistortion,MO2D-DPD模型)。传统双频段模型(Dual-Band Digital Predistortion,2D-DPD模型)中偶次项对模型精度影响不大,本文提出的新模型由奇次记忆多项式模型推导构建,达到相同线性化效果的前提下,新模型相比2D-DPD模型参数大大减少,模型复杂度降低。经测试当两种模型(5阶记忆深度为2)预失真输出ACPR改善都在20dBc以上时,而新模型参数减少了一半以上。
[Abstract]:Nowadays, power amplifiers (Power Amplifier,PA) which support concurrent dual-band are appeared one after another, but the traditional dual-band predistortion model is difficult to implement because of its high complexity. In this paper, a new simplified digital predistortion model (Modified Odd Dual-Band Digital Predistortion,MO2D-DPD model is proposed for the special nonlinear characteristics of concurrent dual-band power amplifiers. In the traditional dual-band model (Dual-Band Digital Predistortion,2D-DPD model), the even-order term has little effect on the precision of the model. The new model proposed in this paper is derived from the odd memory polynomial model and achieves the same linearization effect. Compared with the 2D-DPD model, the parameters of the new model are greatly reduced and the complexity of the model is reduced. It is tested that when the ACPR of the two models (5 order memory depth 2) is improved by more than 20dBc, the parameters of the new model are reduced by more than half.
【作者单位】： 南京理工大学;
【基金】：国家自然科学基金青年项目(61301020) 江苏省自然科学基金青年项目(BK20130772) 江苏高校优势学科建设工程资助项目
【分类号】：TN722.75
[Abstract]:Nowadays, power amplifiers (Power Amplifier,PA) which support concurrent dual-band are appeared one after another, but the traditional dual-band predistortion model is difficult to implement because of its high complexity. In this paper, a new simplified digital predistortion model (Modified Odd Dual-Band Digital Predistortion,MO2D-DPD model is proposed for the special nonlinear characteristics of concurrent dual-band power amplifiers. In the traditional dual-band model (Dual-Band Digital Predistortion,2D-DPD model), the even-order term has little effect on the precision of the model. The new model proposed in this paper is derived from the odd memory polynomial model and achieves the same linearization effect. Compared with the 2D-DPD model, the parameters of the new model are greatly reduced and the complexity of the model is reduced. It is tested that when the ACPR of the two models (5 order memory depth 2) is improved by more than 20dBc, the parameters of the new model are reduced by more than half.
【作者单位】： 南京理工大学;
【基金】：国家自然科学基金青年项目(61301020) 江苏省自然科学基金青年项目(BK20130772) 江苏高校优势学科建设工程资助项目
【分类号】：TN722.75
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