射频功率放大器行为模型基函数自适应生成技术

钟天旸; 彭俊; 何松柏; 游飞; 王鑫宇; 梁宸瑞

doi:10.12263/DZXB.20260304

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射频功率放大器行为模型基函数自适应生成技术

射频计算 | 更新时间：2026-10-16

- 射频功率放大器行为模型基函数自适应生成技术
- Adaptive Basis Function Generation Technique for Behavioral Model of RF Power Amplifier
- 电子学报 2026年54卷第6期页码：2481-2492
- 作者机构：
  
  电子科技大学电子科学与工程学院，四川成都 611731
- 作者简介：
  
  钟天旸男，2001年8月出生于四川省自贡市。现为电子科技大学电子科学与工程学院博士研究生。主要研究方向为射频功率放大器行为模型建模与数字预失真。E-mail: tianyangzhong@foxmail.com
  彭俊男，1990年8月出生于四川省自贡市。分别于2013年和2019年获电子科技大学学士和博士学位。现任电子科技大学副研究员。主要研究方向为射频无线收发系统、射频前端数字化与集成化以及中射频算法。E-mail: junpeng@uestc.edu.cn
  何松柏男，1971年12月出生于四川省巴中市。分别于1995年、1998年和2003年获电子科技大学学士、硕士和博士学位。现任电子科技大学电子科学与工程学院教授、博士生导师。主要研究方向为射频/微波电路与系统、频率合成、无线通信以及非线性动力系统。E-mail: sbhe@uestc.edu.cn
  游飞男，1982年1月出生于重庆市。分别于2004年、2009年获电子科技大学学士、博士学位。现任电子科技大学教授、博士生导师。主要研究方向为高效率功率放大器设计、高效率宽带直流调制器、射频-直流转换、数字功率放大器以及射频CMOS集成电路设计。E-mail: feiyou@uestc.edu.cn
  王鑫宇男，1999年9月出生于安徽省滁州市。现为电子科技大学电子科学与工程学院博士研究生。主要研究方向为射频功率放大器行为数字预失真与数字谐波抑制。E-mail: yuuuu_wang@foxmail.com
  梁宸瑞男，2000年11月出生于湖南省湘西土家族苗族自治州。现为电子科技大学电子科学与工程学院博士研究生。主要研究方向为功率放大器数字预失真技术和数字谐波抑制技术。E-mail: liangcr2024@foxmail.com
- 基金信息：
  
  国家自然科学基金(62571092;62271114)
- DOI：10.12263/DZXB.20260304
  中图分类号： TN911.72;
- 收稿：2026-04-17，
  
  录用：2026-05-05，
  
  纸质出版：2026-06-25
- 稿件说明：
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钟天旸, 彭俊, 何松柏, 等. 射频功率放大器行为模型基函数自适应生成技术[J]. 电子学报, 2026, 54(06): 2481-2492.

ZHONG Tianyang, PENG Jun, HE Songbai, et al. Adaptive Basis Function Generation Technique for Behavioral Model of RF Power Amplifier[J]. Acta Electronica Sinica, 2026, 54(06): 2481-2492.
钟天旸, 彭俊, 何松柏, 等. 射频功率放大器行为模型基函数自适应生成技术[J]. 电子学报, 2026, 54(06): 2481-2492. DOI：10.12263/DZXB.20260304

ZHONG Tianyang, PENG Jun, HE Songbai, et al. Adaptive Basis Function Generation Technique for Behavioral Model of RF Power Amplifier[J]. Acta Electronica Sinica, 2026, 54(06): 2481-2492. DOI：10.12263/DZXB.20260304

摘要

随着5G/6G通信技术的演进，高阶调制和宽带（百MHz量级）信号的应用导致射频功率放大器（Power Amplifiers，PAs）被迫工作在接近饱和的区域，从而呈现出显著的强记忆效应与非线性特性。为了在保证高能效的同时完成高线性度指标，数字预失真（Digital PreDistortion，DPD）已成为基带处理端的关键技术。而传统的记忆多项式行为模型受限于固定结构，其核系数数量随着记忆深度和非线性阶数的增加呈指数级增长，面临严重的“维数灾难”，使得数字预失真系统的实现复杂度较高。同时，现有的动态生成模型依然未脱离传统多项式乘积机制，在宽带场景下易引发字典空间急剧膨胀与巨大的计算开销等问题。针对上述问题，本文提出了一种基于线性B样条基的功放行为模型基函数自适应生成技术。该技术摒弃了繁冗的高阶多项式乘积运算，以具有严格局部支撑性和良好数值稳定性的线性B样条函数为基础构建单元，引入基于子空间系数重估的贪婪迭代搜索策略，通过探测残差方向自适应构建与特定记忆深度匹配的幅度增益函数。同时，该技术将非线性幅度映射扩展至复数域，显著增强了对宽带信号复杂幅相失真的捕获能力，从而实现对功率放大器特性的精确拟合。在实现层面，该生成技术充分发挥B样条函数局部渐进逼近的优势，其构建的模型可简化为硬件友好的查找表（Look-Up Table， LUT）配合线性插值的结构。这种结构成功将复杂的非线性代数计算转化为底层存储位置的索引与简单的加乘组合，在保证精度的同时显著降低了计算复杂度与乘法器逻辑资源的消耗。为了验证所提技术的性能，本文基于100 MHz宽带信号和功放搭建了测试平台。实验结果表明，在保证与传统模型相当的线性化性能下，模型系数缩减了近90%，实现复杂度减少了74%，提升了行为建模和数字预失真效率。最后，通过对不同非线性特性功放的扩展测试，结果表明该自适应算法不依赖特定功放的物理先验特征，进一步证实所提模型具备较好的泛化能力，且易于在实际工程应用。

Abstract

With the evolution of 5G/6G communication technologies

the application of high-order modulation and wideband signals (in the order of hundreds of MHz) forces radio frequency (RF) power amplifiers (PAs) to operate in near-saturation regions

thereby exhibiting significant strong memory effects and nonlinear characteristics. To achieve high linearity specifications while maintaining high energy efficiency

digital predistortion (DPD) has become a key technology at the baseband processing end. However

traditional memory polynomial behavioral models are constrained by their fixed structures. The number of their kernel coefficients grows exponentially with the increase of memory depth and nonlinearity order

leading to a severe “curse of dimensionality” and resulting in high implementation complexity for DPD systems. Furthermore

existing dynamic generation models still rely on traditional polynomial multiplication mechanisms

which are prone to causing rapid expansion of the dictionary space and massive computational overhead in wideband scenarios. To address these issues

this paper proposes an adaptive basis function generation technique for PA behavioral models based on linear B-spline basis. This technique abandons cumbersome high-order polynomial multiplication operations

utilizing linear B-spline functions—which possess strict local support and excellent numerical stability—as fundamental building blocks. It introduces a greedy iterative search strategy based on subspace coefficient re-estimation to adaptively construct amplitude gain functions that match specific memory depths by detecting the residual direction. Additionally

this technique extends the nonlinear amplitude mapping to the complex domain

significantly enhancing the capability to capture the complex amplitude and phase distortions of wideband signals

thereby achieving precise fitting of PA characteristics. At the implementation level

by fully leveraging the local asymptotic approximation advantages of B-spline functions

the model constructed by this generation technique can be simplified into a hardware-friendly structure combining look-up tables (LUTs) with linear interpolation. This structure successfully transforms complex nonlinear algebraic computations into underlying memory location indexing and simple addition-multiplication combinations

significantly reducing computational complexity and multiplier logic resource consumption while maintaining accuracy. To verify the performance of the proposed technique

a test platform is constructed based on a 100 MHz wideband signal and PAs. Experimental results demonstrate that

while maintaining linearization performance comparable to traditional models

the proposed method reduces the number of model coefficients by nearly 90% and decreases implementation complexity by 74%

significantly improving the efficiency of both behavioral modeling and DPD. Finally

extended tests on PAs with varying nonlinear characteristics indicate that the adaptive algorithm does not rely on the physical a priori features of specific PAs. This further confirms that the proposed model possesses good generalization capabilities and is easy to apply in practical engineering.

关键词

Keywords

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