功率放大器非线性特性及预失真建模研究

针对无记忆功率放大器的非线性特性及预失真建模的问题,首先建立了多项式模型、极坐标Saleh模型和基于正交三角函数的模型并利用MATLAB对其进行了求解,然后给出了无记忆多项式预失真处理器特性函数表达式及最小二乘解.针对记忆功率放大器的非线性特性及预失真建模的问题,首先建立了记忆多项式模型并对其进行了求解,然后建立了相应的有记忆多项式预失真模型并利用最小二乘法进行了求解,并提出了联合功率放大器特性和输入信号幅值范围的有记忆功放自适应预失真模型.最后求出所给输入信号、输出信号以及加入预失真后线性系统的输出信号的功率谱密度,并计算和比较了信道的带外失真参数ACPR;结果显示,加入预失真后大大提升了系统的性能,线性特性明显加强.     

功率放大器非线性特性及预失真建模

信号的功率放大器是电子通信系统的关键器件之一,功放的输出信号相对于输入信号可能产生非线性变形,这将带来无益的干扰信号,研究其机理并采取措施改善,具有重要意义.通过利用无记忆非线性功放和记忆非线性功放的实测数据用数学方法对其分别进行建模,而后使用前置预失真器的方法改善功放的非线性特性,并对其中预失真器的建模做了研究,采用间接学习型结构构建预失真器模型.仿真结果显示功放非线性模型结合预失真器模型能够很好地逼近实际情况,并且能很好地抑制带外频谱扩展.     

运用多目标规划优化功放的预失真模型

预失真技术是克服功率放大器非线性失真的一种非常有效的方法.许多研究者从数学建模的思想角度出发,建立了关于功放与预失真的各类级数模型,以实现线性增益输出.但是在应用通信系统中,这些模型不能有效地动态实现功放效率尽可能高的要求.在和记忆多项式模型的基础上,创新地运用多目标规划的方法优化功放的预失真模型.在满足一定的线性化输出的同时,尽可能地得到更高的功放效率,而且能同时计算出增益系数与模型参数.对于数据量为1000的无记忆功放得到增益倍数为1.82649,预失真模型的NMSE值为-41.5633.数据量为73920的有记忆功放得到增益倍数为9.5969,预失真模型的NMSE值为-24.3682.     

功率放大器失真问题研究及预失真建模

电子器件中的功率放大器常常伴随着非线性失真效应.为解决此类非线性失真问题,通过研究无记忆和有记忆功放的失真特性,运用最小二乘法来构建多种形式的特性拟合函数,在选择了效果较好的特性拟合函数基础上,根据实际约束条件进行预失真模型的建立,使失真处理后的输出信号趋于线性,最后从信号的功率谱密度的角度出发检验预失真模型的补偿效果,证明模型具有较好的可行性和准确性.     

2013年研究生数模竞赛B题综述

宽带移动通信传输正在改变着人们的生活.作为信息传输的重要环节,信号的功率放大在无线通信中起着关键作用.第十届全国研究生数模竞赛B题:功率放大器非线性特性及预失真建模,利用数学建模的思路辨识功率放大器非线性失真行为模型并通过预畸变来减少失真.通信系统的发展需要有技术弥补功率器件的固有非线性特性,保证功率放大器的能量转换效率和线性度.随着器件的更新和信息传输速率的增加,解决此问题的数学模型—预失真算法一直在更新和发展.在研究生数模竞赛中引入此类赛题,可以开阔思维,激发研究生使用数学的兴趣,提高利用数学模型解决实际工程问题的能力,同时利用学生思维活跃的优势,提出有价值的创新点,丰富现有的算法类型,为国内相关企业研究解决此类问题提供新的思路方法,具有重要的意义.     

基于FIDC模型变点检测的美国次贷危机传染分析

现有文献主要讨论单个市场收益的长记忆性质或者市场间收益的相依性,但都没有将上述两个问题联系起来进行研究.与以往研究不同,文章将研究多个国家的股票市场收益与美国股票市场收益之间尾部相依性的长记忆问题.这不仅能检验市场的有效性,还有助于刻画国际市场间的相依结构,观察危机传染的特征并做出预警.为了刻画市场间尾部相依长记忆性的动态变化结构,文章对现有模型加以改进,提出了长记忆性的变点检测方法.从变点的角度实证分析了中国、日本、德国和法国股市与美国股市之间尾部相依的长记忆性,进而研究了次贷危机的传染现象.实证结果表明,文章提出的模型在分析金融传染方面是有效的,在不同的股市之间的实证结果具有相似性.第一变点和第二变点的时刻分别与次贷危机的结束和开始时间相对应,基于两个变点将数据分为三段对估计结果进行分析发现,下尾相依系数在危机传染期间明显变大,而且相依性的长记忆性也明显增强,说明次贷危机对所分析的国家具有传染效应.     

基于一类长记忆过程的经济时间序列建模研究

本文介绍了长记忆的概念 ,首次提出了一种与 ADF检验相结合的长记忆性判断方法。给出了将参数 d的初估计与近似极大似然估计相结合 ,将时间序列长记忆分析与短记忆分析相结合的建立ARFIMA模型的方法。论文进行了实证研究 ,并证明了该模型与其它模型相比的优效性     

基于贝叶斯ARFIMA-WRV模型高频数据长记忆性研究

考虑到高频时间序列波动率的长记忆性问题,构建了赋权已实现波动分数整合自回归移动平均(ARFIMA-WRV)模型对其进行了研究.利用贝叶斯统计方法对模型做了相应的贝叶斯分析,并对我国中小板股市收益波动率的长记忆性特征进行了实证分析.实证结果表明我国中小板股市收益波动率存在长记忆性特征;采用消除日历效应影响的赋权已实现波动作为波动度量和贝叶斯参数估计方法,很大程度上提高了模型的参数精度.     

空间分数阶非Newton流体本构及圆管流动规律研究

对非Newton流体的本构及流动规律进行研究是分析、预测和控制非Newton流体在管道中流动的关键.实验表明非Newton流体在流动过程中具有历史记忆性,基于空间分数阶微积分方法,建立了分数阶非Newton流体本构模型;并推导了该模型在圆管中的流速分布、流量、平均流速、压降、平均Reynolds数等管道流动参数;提出了分数阶非Newton流体圆管流态判别准则.研究表明非Newton流体的圆管流层间的切应力可以通过流速的轴向分布大小来描述.对于不含屈服切应力的分数阶非Newton流体,分数阶的阶数越大,断面流速分布越均匀,记忆能力越强.分数阶的阶数大小反映了流体对全域空间的记忆性强弱;而对于含有屈服切应力的分数阶非Newton流体,分数阶的阶数越大,速梯区流速分布越均匀,流核区速度越小.分数阶的阶数大小反映了局部空间记忆性强弱.该研究为非Newton流体的记忆特征提供了一种新的建模方法.     

基于小波变换的LMSV模型对人民币汇率波动的研究

将小波引入到LMSV模型波动长记忆性的估计与检验中，提出了基于小波变换的LMSV模型波动长记忆性的伪极大似然估计法和波动长记忆性的检验方法，并对各汇率波动序列长记忆效应的大小程度进行了验证．结果表明各汇率波动序列存在长记忆效应．人民币对美元的汇率波动序列受历史信息的影响程度最高．     

A nonlinearity predistortion technique for HPA with memory effects in OFDM systems

Memory effects of high power amplifiers (HPAs) can no longer be ignored when input signals have relatively broad bandwidth. In this paper, a new adaptive method called two-dimensional look up table (2D LUT) is presented to linearize HPAs with memory effects. Different predistortion factors are distributed to the same current inputs with different historical states, in order to eliminate the memoryless characteristic of 1D LUT. Simulation and experimental results show that 2D LUT suppresses the signal distortion more effectively with a linearity improvement of 20 dB.     

A stochastic conjugate gradient method for the approximation of functions

A stochastic conjugate gradient method for the approximation of a function is proposed. The proposed method avoids computing and storing the covariance matrix in the normal equations for the least squares solution. In addition, the method performs the conjugate gradient steps by using an inner product that is based on stochastic sampling. Theoretical analysis shows that the method is convergent in probability. The method has applications in such fields as predistortion for the linearization of power amplifiers.     

Intuitionistic uncertain linguistic powered einstein aggregation operators and their application to multi-attribute group decision making

The intuitionistic uncertain fuzzy linguistic variable can easily expressthe fuzzy information, and the power average (PA) operator is a usefultool which provides more versatility in the information aggregation procedure.At the same time, Einstein operations are a kind of various t-normsand t-conorms families which can be used to perform the corresponding intersectionsand unions of intuitionistic fuzzy sets (IFSs). In this paper, wewill combine the PA operator and Einstein operations to intuitionistic uncertainlinguistic environment, and propose some new PA operators. Firstly,the definition and some basic operations of intuitionistic uncertain linguisticnumber (IULN), power aggregation (PA) operator and Einstein operationsare introduced. Then, we propose intuitionistic uncertain linguistic fuzzypowered Einstein averaging (IULFPEA) operator, intuitionistic uncertain linguisticfuzzy powered Einstein weighted (IULFPEWA) operator, intuitionisticuncertain linguistic fuzzy Einstein geometric (IULFPEG) operator and intuitionisticuncertain linguistic fuzzy Einstein weighted geometric (IULFPEWG)operator, and discuss some properties of them in detail. Furthermore, we developthe decision making methods for multi-attribute group decision making(MAGDM) problems with intuitionistic uncertain linguistic information andgive the detail decision steps. At last, an illustrate example is given to showthe process of decision making and the effectiveness of the proposed method.     

组合GM(1,1)幂模型及其应用

GM(1,1)幂模型是灰色Verhulst模型的推广.由于初始条件选取影响GM(1,1)幂模型的精度,将平均相对误差函数分别看成是幂指数、发展系数、灰作用量的函数,利用蚁群算法进行参数辨识,从而建立多个单项GM(1,1)幂模型.利用这些单项模型建立了线性组合GM(1,1)幂模型,组合权系数利用最大相对误差最小化原则采用粒子群算法确定.实例表明,组合GM(1,1)幂模型的建模精度高于传统GM(1,1)幂模型,同时也说明方法是有效的和可行的,具有重要的理论意义.     

Project advancement and its applications to multi-air-route quality budget allocation

In this paper, we propose a problem-solving tool referred to as ‘Project Advancement (PA)’. The focal point of PA is to aid managers to: (1) identify the problem-solving situation correctly with which they are faced and (2) build an effective resource-planning model to minimize their disappointment and frustration. PA suggests a specific seven-step list to realize such a focal point. In order to help readers to understand PA comprehensively, this paper utilizes a simple scenario referring to the case of air transportation between Taiwan and China. We explain how to apply PA to solving a multi-air-route, quality-improvement, and budget-allocation problem through step-by-step deduction. This paper then suggests the extra supplement and the contribution of ‘PA’.     

Linguistic power aggregation operators and their application to multiple attribute group decision making

The aim of this study is to develop new linguistic aggregation operators based on the power-average (PA) operator, such as the linguistic power average (LPA) operator, the linguistic weighted PA operator, and the LPOWA operator. We studied some desired properties of the developed operators, such as idempotency and commutativity. Moreover, we developed two approaches to deal with group decision-making problems under linguistic environments. If the weighting vector of the decision makers was known, we developed an approach based on the linguistic weighted PA operator. On the other hand, if the weighting vector of the decision maker was unknown, we developed a different approach based on the LPOWA operator. We also developed new uncertain linguistic operators under uncertain linguistic environments, such as the ULPA operator, the uncertain linguistic weighted PA operator, and the ULPOWA operator. We extended these approaches, which are based on ULWPA and ULPOWA operators, respectively, to solve group decision-making problems under an uncertain linguistic environment. Finally, a practical example is provided to illustrate the multiple-attribute group decision-making process.