DOI码：10.1109/TMTT.2022.3167669

所属单位：中国科学技术大学电子工程与信息科学系

发表刊物：IEEE Transactions on Microwave Theory and Techniques

项目来源：国家自然科学基金 61471333

关键字：Band-limited feedback, digital predistortion (DPD), manifold regularization (MR), power amplifiers (PAs), sampling rate reduction.

摘要：Digital predistortion (DPD) is an effective linearization technique for RF power amplifiers (PAs), but conventional full sampling (FS) DPD systems use ADCs with three to five times signal bandwidth, and high-speed ADCs are expensive and power-hungry. In this article, we develop a novel bandlimited DPD for reducing feedback sampling rate and acquisition bandwidth based on a general framework for semisupervised learning called manifold regularization (MR), which utilizes the geometry of unlabeled data to construct regularization terms for mitigating the overfitting problem. Considering the properties of DPD, we design a basis MR term and introduce it into the classical MR to obtain the extended MR (ExMR) method. To validate the proposed ExMR DPD method, experiments were conducted on two different RF PAs operating at 2.4 and 39 GHz, respectively. The test results demonstrate that the proposed ExMR DPD can linearize the RF PA with a 40-MHz acquisition bandwidth at 100-MHz input. The proposed method significantly reduces the cost and power consumption of the DPD system in comparison with the conventional FS DPD method, which provides a promising solution for broadband communication systems.

合写作者：Wen Qiao,Guichen Yang,Lei Su,Renlong Han,Jingchao Tan

第一作者：Chengye Jiang (姜成业)

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2022.3167669

学科门类：工学

文献类型：J

卷号：70

期号：11

页面范围：4928-4939

是否译文：否

发表时间：2022-11-01

收录刊物：SCI、EI