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

doi:10.3969/j.issn.0372-2112.2018.09.015

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

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. 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
- 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.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
- Corresponding author:
- SU Kai-xiong
- Supported by:
- National Natural Science Foundation of China (No.61401099); Program of Education Department of Fujian Provicne (No.JAT170087)

摘要/Abstract

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

中图分类号:

TN911.72

吴林煌, 苏凯雄, 王琳, 等. 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[J]. 电子学报, 2018, 46(9): 2149-2156.

WU Lin-huang, SU Kai-xiong, WANG Lin, et al. Adaptive Dual-band Predistortion Model Optimization Algorithm Based on Orthogonal Matching Pursuit and Bayesian Information Criterion[J]. Acta Electronica Sinica, 2018, 46(9): 2149-2156.

参考文献

[1] Qian H,Yao S,Huang H,et al.Low complexity coefficient estimation for concurrent dual-band digital predistortion[J].IEEE Transactions on Microwave Theory and Techniques,2015,63(10):3153-3163.
[2] 邓海林,张德伟,白维达,等.一种行波管用毫米波射频预失真线性化器[J].电子学报,2017,45(7):1784-1791. DENG Hai-lin,ZHANG De-wei,BAI Wei-da,et al.A millimeter-wave RF predistortion linearizer for travelling wave tube amplifier[J].Acta Electronica Sinica,2017,45(7):1784-1791.(in Chinese)
[3] Bassam S A,Helaoui M,Ghannouchi F M.2-D digital predistortion (2-D-DPD) architecture for concurrent dual-band transmitters[J].IEEE Transactions on Microwave Theory and Techniques,2011,59(10):2547-2553.
[4] 黄春晖,温永杰.用记忆型BP神经网络实现HPA预失真的算法研究[J].通信学报,2014,35(1):16-23. HUANG Chun-hui,WEN Yong-jie.Algorithm study of digital HPA predistortion using one novel memory type BP neural network[J].Journal on Communications,2014,35(1):16-23.(in Chinese)
[5] Eriksson T,Fager C.Digital predistortion of concurrent multiband communication systems[A].2014 IEEE International Conference on Acoustic,Speech and Signal Processing (ICASSP)[C].Florence:IEEE,2014.3918-3922.
[6] Liu Y J,Chen W,Zhou J,et al.Digital predistortion for concurrent dual-band transmitters using 2-D modified memory polynomials[J].IEEE Transactions on Microwave Theory and Techniques,2013,61(1):281-290.
[7] Hammi O,Abdelhafiz A,Ghannouchi F M,et al.On the use of compressed sampling algorithms for impairments compensation in dynamic nonlinear transmitters[A].2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)[C].Abu Dhabi:IEEE,2015.641-645.
[8] Zenteno E,Amin S,Isaksson M,et al.Combating the dimensionality of nonlinear MIMO amplifier predistortion by basis pursuit[A].201444th European Microwave Conference (EuMC)[C].Rome:IEEE,2014.833-836.
[9] Wisell D,Jaldén J,Handel P.Behavioral power amplifier modeling using the LASSO[A].2008 Instrumentation and Measurement Technology Conference Proceedings(IMTC)[C].Victoria:IEEE,2008.1864-1867.
[10] Gilabert P L,Montoro G,López D,et al.Order reduction of wideband digital predistorters using principal component analysis[A].2013 IEEE MTT-S International Microwave Symposium Digest (IMS)[C].Seattle:IEEE,2013.1-7.
[11] Abdelhafiz A,Kwan A,Hammi O,et al.Digital predistortion of LTE-A power amplifiers using compressed-sampling-based unstructured pruning of Volterra series[J].IEEE Transactions on Microwave Theory and Techniques,2014,62(11):2583-2593.
[12] Li M,Yang Z,Zhang Z,et al.Sparsity adaptive estimation of memory polynomial based models for power amplifier behavioral modeling[J].IEEE Microwave and Wireless Components Letters,2016,26(5):370-372.
[13] 杨霖,何向东,丁长文.OFDM系统中基于限幅和压缩感知的非线性失真补偿算法[J].电子学报,2016,44(9):2051-2056. YANG Lin,HE Xiang-dong,DING Chang-wen.Compensation method for nonlinear distortion based on clipping and compressive sensing in OFDM system[J].Acta Electronica Sinica,2016,44(9):2051-2056.(in Chinese)
[14] Schwarz G.Estimating the dimension of a model[J].The Annals of Statistics,1978,6(2):461-464.
[15] 刘伟峰,杨爱兰.基于BIC准则和Gibbs采样的有限混合模型无监督学习算法[J].电子学报,2011,39(3):134-139. LIU Wei-feng,YANG Ai-lan.Unsupervised learning for finite mixture models based on BIC criterion and Gibbs sampling[J].Acta Electronica Sinica,2011,39(3):134-139.(in Chinese)
[16] Lee E R,Noh H,Park B U.Model selection via Bayesian information criterion for quantile regression models[J].Journal of the American Statistical Association,2014,109(505):216-229.
[17] Shi M,Shen W,Wang H Q,et al.Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach[J].IET Systems Biology,2016,10(6):252-259.
[18] Selim B,Alhussein O,Muhaidat S,et al.Modeling and analysis of wireless channels via the mixture of Gaussian distribution[J].IEEE Transactions on Vehicular Technology,2016,65(10):8309-8321.
[19] Stoica P,Babu P.On the proper forms of BIC for model order selection[J].IEEE Transactions on Signal Processing,2012,60(9):4956-4961.
[20] Bishop C M.Pattern Recognition and Machine Learning[M].New York:Springer Press,2006.24~32,78~113.

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

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

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