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.
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.
作者
吴林煌
苏凯雄
王琳
陈志峰
陈平平
WU Lin-huang;SU Kai-xiong;WANG Lin;CHEN Zhi-feng;CHEN Ping-ping(College of Physics and Information Engineering,Fuzhou University,Fuzhou,Fujian 350116,China;College of Information Science and Technology,Xiamen University,Xiamen,Fujian 361005,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2018年第9期2149-2156,共8页
Acta Electronica Sinica
基金
国家自然科学基金项目(No.61401099)
福建省教育厅项目(No.JAT170087)
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
power amplifier
predistortion
sparsity
orthogonal matching pursuit
Bayesian information criterion
