电子学报 ›› 2018, Vol. 46 ›› Issue (9): 2149-2156.DOI: 10.3969/j.issn.0372-2112.2018.09.015

• 学术论文 • 上一篇    下一篇

正交匹配追踪和BIC准则的自适应双频段预失真模型优化算法

吴林煌1, 苏凯雄1, 王琳2, 陈志峰1, 陈平平1   

  1. 1. 福州大学物理与信息工程学院, 福建福州 350116;
    2. 厦门大学信息科学与技术学院, 福建厦门 361005
  • 收稿日期:2017-03-16 修回日期:2017-12-25 出版日期:2018-09-25 发布日期:2018-09-25
  • 通讯作者: 苏凯雄
  • 作者简介:吴林煌 男,1984年12月生于福建漳州,博士,助理研究员,研究方向为数字预失真、基带信号处理等.E-mail:wlh173@163.com;王琳 男,1963年6月生于重庆,教授,博士生导师,研究方向为无线通信系统算法、信源信道联合编码等.E-mail:wanglin@xmu.edu.cn;陈志峰 男,1979年9月生于福建莆田,教授,博士生导师,研究方向为无线通信、视频编码、人工智能等.E-mail:zhifeng@fzu.edu.cn;陈平平 男,1986年12月生于福建泉州,教授,研究方向为联合信源信道编码、网络编码等.E-mail:chenpingping_xmu@qq.com
  • 基金资助:
    国家自然科学基金项目(No.61401099);福建省教育厅项目(No.JAT170087)

Adaptive Dual-band Predistortion Model Optimization Algorithm Based on Orthogonal Matching Pursuit and Bayesian Information Criterion

WU Lin-huang1, SU Kai-xiong1, WANG Lin2, CHEN Zhi-feng1, CHEN Ping-ping1   

  1. 1. College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350116, China;
    2. College of Information Science and Technology, Xiamen University, Xiamen, Fujian 361005, China
  • Received:2017-03-16 Revised:2017-12-25 Online:2018-09-25 Published:2018-09-25

摘要: 针对双频段预失真模型复杂度高以及当前的模型优化算法不具有自适应性的问题,提出一种自适应的模型优化算法.采用双频段广义记忆多项式作为预失真模型,通过正交匹配追踪算法对原始模型的基函数项进行排序,每次迭代时用所有已挑选的基函数项构成备选模型,推导了模型输出向量元素服从非独立同分布情况下的贝叶斯信息准则(Bayesian Information Criterion,BIC),并将BIC值最小的备选模型作为优化后模型,从而在原始模型稀疏度和拟合误差门限未知情况下,实现了模型的自适应优化.结果表明:优化后模型与原始模型相比,二者分别预失真后的信号在邻道功率比和归一化均方误差方面均非常接近,预失真效果良好,而模型的系数量减少了75%以上.

关键词: 功率放大器, 预失真, 稀疏性, 正交匹配追踪, 贝叶斯信息准则

Abstract: The dual-band predistortion models suffer from high complexity and non-adaptability of optimization algorithms.To address this issue,this paper proposes an adaptive optimization algorithm for dual-band predistortion model with reduced complexity.We use dual-band general memory polynomial (DB-GMP) as the predistortion model where all basis function terms of the original DB-GMP model are sorted by orthogonal matching pursuit algorithm.In each iteration,all selected basis function terms help to construct an alternative model.We then derive the Bayesian information criterion (BIC) when output vector elements of the DB-GMP model are with non-independent identical distributions,and the model with smallest BIC value is treated as the optimized model.Finally,we achieve the proposed algorithm without the information of model sparsity and fitting error threshold.Simulation results show that compared with the original DB-GMP model,the coefficient number of the optimized model is reduced by more than 75%,while both the models after predistortion have almost the same level of adjacent channel power ratio and normalized mean squared error,leading to good predistortion performance.

Key words: power amplifier, predistortion, sparsity, orthogonal matching pursuit, Bayesian information criterion

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