版权所有:内蒙古大学图书馆 技术提供:维普资讯• 智图
内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Univ Nantes UMR CNRS 6164 Inst Elect & Telecommun Rennes F-44035 Nantes France Telerad Co F-64603 Anglet France
出 版 物:《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS》 (IEEE电路与系统汇刊第一部分:基础理论与应用)
年 卷 期:2019年第66卷第3期
页 面:1031-1041页
核心收录:
基 金:BPI France region Occitanie region Nouvelle Aquitaine, through the APOGEES project
主 题:Nonlinear systems power amplifier adaptive digital predistortion (DPD) indirect learning architecture (ILA) direct learning architecture (DLA) adaptive algorithm
摘 要:This paper presents a novel adaptive digital predistortion (DPD) technique based on a cascade of an adaptive indirect learning architecture (ILA) and a static direct learning architecture (DLA) using a linear interpolation look-up-table (LILUT). The static LILUT-DLA-based DPD is designed to identify the inverse of a radio-frequency power amplifier (PA) model. The cascaded system of the DLA-based predistorter (PD) and PA is theoretically linear. However, in real-time applications, the PA characteristics change with time due to process, supply voltage, and temperature variations, making this cascaded system not strictly linear, which results in some residual nonlinear distortion at the PA output. This residual distortion is effectively compensated by an additional adaptive HA-based PD using least mean squares or recursive least squares. Thanks to the incorporation of the static DLA, the proposed DPD approach is less sensitive to the PA output noise, ensuring a better preinverse of the PA and also requiring a smaller number of adaptive coefficients than either the adaptive stand-alone DLA- or ILA-based DPDs. The experimental results show that the proposed DPD technique effectively linearizes the PA, even if its characteristics change, and obtains better linearization performance than either the classical stand-alone DLA- or stand-alone ILA-based DPDs.