MIMO-STBC系统参数盲估计研究

发布时间：2018-04-13 01:24

  本文选题：多输入多输出 + 空时分组码　； 参考：《电子科技大学》2014年博士论文

【摘要】：近年来,随着多媒体技术和Internet的发展,人们对无线通信的质量和通信速率提出了越来越高的要求。多输入多输出(Multiple Input Multiple Output,MIMO)技术可以有效地提高信道容量,是这些问题的有效解决方案。空时编码(Space-Time Coding,STC)在发射的时域信号中引入编码冗余而得到分集增益,而空时分组码(Space-Time Block Code,STBC)是空时编码的主要类型。作为下一代无线通信的关键技术之一,IEEE 802.11n和IEEE 802.16e已经对MIMO-STBC通信系统制定了一系列的标准。通信系统参数盲估计广泛应用于军事及民用通信系统中,是一个重要的研究领域。目前对合作MIMO-STBC通信系统的研究较多,而很少有文献从非合作角度对MIMO-STBC系统进行参数估计。独立分量分析(Independent Component Analysis,ICA)是上个世纪九十年代发展起来的一种重要的信号处理方法,不像其他方法,独立分量分析不需要很强的先验假设,因此是非合作信号处理的有力工具之一。本文以ICA算法为工具,从非合作角度对MIMO-STBC系统参数盲估计进行了研究,主要成果有:1、提出了一种适用于空间色噪声环境的信源数目估计算法。信源个数是MIMO-STBC系统的一个重要参数,文献中的经典算法大多假设噪声在空间上不相关,然而对于受干扰的MIMO信道通常假设噪声在空间相关,所以这些算法不能适用于这种场景。本文利用接收信号的高阶累积量矩阵的对角特性,通过特征值分解估计出噪声子空间,经过酉变换后,对应于噪声子空间的信号分量服从高斯分布。本文以峭度为统计量,通过假设检验来检测信号的高斯性,从而估计出信源数目。该方法适用于空间色噪声,具有较好的检测性能。2、研究了空时分组码在高维特征空间中的分类识别问题。本文利用空时分组码的循环平稳特性,对接收到的信号在多个时延上做自相关,对于不同的空时分组码,这些自相关矩阵的二范数具有不同的峰值和谷点,所以这些峰值和谷点携带了编码类型的信息,可以作为特征来区分各种空时分组码。为了更好地利用这些信息,本文将其映射到高维特征空间对空时分组码进行分类。由于利用了更多的信息,所以基于高维特征空间的识别算法的性能优于一维识别算法。3、提出了一种旋转的多天线空时盲分离算法。在合作通信中,由于编码信息已知,可以通过估计信道矩阵来分离接收到的信号。而在非合作系统中,在编码矩阵和信道状态信息(Channel State Information,CSI)都未知时如何分离接收到的信号是一个具有挑战性的问题。本文提出了一个旋转的MIMO-STBC通信模型,该模型将编码矩阵和信道矩阵合并为虚拟信道矩阵(Virtual Channel Matrix,VCM),通过对接收信号旋转一定角度来最大化源信号的独立性,使其满足ICA算法的假设。在此模型上应用ICA算法估计的信号反向旋转相应角度后就可以分离出源信号。该方法无需在发射端进行预编码,并且不需要编码信息。4、研究了多维独立分量分析(Multidimensional Independent Component Analysis,MICA)算法在非合作MIMO-STBC系统中的应用,并提出了一种基于高阶统计量(Higher-Order Statistics,HOS)的MICA算法。为了分离接收到的信号,本文用ICA模型来表示MIMO-STBC系统,然而对一些调制信号,由于源信号成组独立而不能满足ICA算法的独立性假设。针对这种情况,本文采用MICA算法来分离接收到的信号,并利用接收信号的高阶累积量矩阵成块对角的特性提出了一种MICA算法,该算法通过对四阶累积量矩阵联合块对角化(Joint Block-Diagonalization,JBD)估计出混合矩阵。针对文献中基于一步优化的JBD算法收敛性差的问题,本文采用两步优化法求解JBD问题:首先对四阶累积量矩阵进行联合对角化(Joint Diagonalization,JD),然后求解分离信号的配对模糊。为了去除信号的配对模糊,本文将JBD优化问题转化为互累积量的极大化问题,该表达式具有明确的物理意义,表明配对问题可以通过极大化组内信号的互累积量来实现。该方法不需要人为设置门限,可以保证收敛性。5、提出了一种基于最大似然的MIMO-STBC系统的盲调制识别算法。调制类型识别是通信参数估计的一个重要研究课题,然而对非合作MIMO-STBC系统的盲调制识别却鲜有报道。本文首先用ICA模型来表示MIMO-STBC系统,并根据此模型提出一个基于最大似然的调制分类器;然后根据源信号的独立性将调制类型分为独立和成组独立星座;然后分别针对这两种星座讨论了基于MICA的虚拟信道矩阵估计方法;最后在消除了部分模糊后,调制分类器对剩余模糊不敏感,不影响调制分类结果。该算法适用于非合作MIMO-STBC系统,并且有较好的识别性能。
[Abstract]:In recent years, with the rapid development of multimedia technology and Internet, put forward higher requirements of the quality of wireless communication and communication rate. Multiple input multiple output (Multiple Input Multiple Output, MIMO) technology can effectively improve the channel capacity, is an effective solution to these problems. The space-time encoding (Space-Time Coding, STC the introduction of redundancy in the time domain) encoding the AE signal and get the diversity gain and STBC (Space-Time Block Code, STBC) is a main type of space-time encoding. As one of the key technologies in the next generation of wireless communication, IEEE 802.11n and IEEE 802.16e has established a series of standards for MIMO-STBC communication system is widely used. In military and civil communication system in the blind parameter estimation of communication system is an important research field. At present the cooperative MIMO-STBC communication system research more, and there is little literature To estimate the parameters of the MIMO-STBC system from the angle of non cooperation. Independent component analysis (Independent Component, Analysis, ICA) is 90s of last century developed an important signal processing method, unlike other methods, independent component analysis without strong prior assumptions, it is one of the most powerful tools for non cooperative signal processing. Based on the ICA algorithm as the tool of MIMO-STBC system blind parameter estimation from non cooperative point of view, the main achievements are as follows: 1. A method is presented for the spatial colored noise of the source number estimation method. The number of sources is an important parameter of MIMO-STBC system, the classical algorithm in the literature assume that the noise not in space, but for the MIMO channel interference usually assumes that the noise in the spatial correlation, so these algorithms cannot be applied to the scene. By using the received signal High order cumulant diagonal matrix decomposition characteristics, estimate the noise subspace by eigenvalue, by unitary transform, signal components corresponding to the noise subspace obeys Gauss distribution. In this paper the kurtosis for statistics, through the hypothesis test to detect the Gauss signal, so as to estimate the source number. This method is applicable to the spatial colored noise has better detection performance of.2, studies the packet classification problem in high dimensional feature space of space-time. Using STBC cyclostationarity, the autocorrelation of the received signal in a delay, for different space-time block code, the autocorrelation matrix two norm has different peak and valley points, so that the peak and valley points carry the encoding type of information, can be used as features to distinguish between the various space-time block codes. In order to make better use of these information, this paper will reflect the Shoot into high dimensional feature space of space-time block code classification. Due to the use of more information, so the performance of.3 is better than one-dimensional recognition algorithm recognition algorithm for high-dimensional feature space based on a multi antenna space rotation when the blind separation algorithm. In cooperative communications, because the encoding information is known, can the separation of the received signal through the channel estimation matrix. In the non cooperation system, in the encoding matrix and the channel state information (Channel State, Information, CSI) are unknown how to separate the received signal is a challenging problem. This paper proposes a communication model of MIMO-STBC rotation, the model will be encoding matrix and channel matrix into virtual channel matrix (Virtual Channel Matrix, VCM), the independence of the received signal is rotated to a certain angle to maximize the source signals, which can meet the ICA algorithm in this hypothesis. The corresponding signal reverse rotation angle model based on ICA estimation algorithm can separate the source signals. This method does not require pre encoding at the transmitter, and does not require the information encoding.4 of multidimensional independent component analysis (Multidimensional Independent Component Analysis MICA) algorithm is applied to the non cooperative MIMO-STBC system, and put forward a method based on high order (Higher-Order Statistics, HOS) MICA algorithm. In order to receive the signal separation, this paper used the ICA model to represent the MIMO-STBC system, but some of the modulation signal as the source signal group independent and can not meet the independence assumption of the ICA algorithm. In view of this situation, this paper to separate the received signal using MICA algorithm, and using the received signal of high order cumulant matrix into a block diagonal of the characteristics of a MICA algorithm is proposed, the algorithm based on four order cumulant Matrix joint block diagonalization (Joint Block-Diagonalization, JBD). The mixing matrix estimation for JBD algorithm convergence step optimization problem based on the difference in the literature, this paper adopts two step optimization method to solve the JBD problem: firstly, the joint diagonalization of four order cumulant matrix (Joint, Diagonalization, JD), then for signal separation the fuzzy matching. In order to remove the signal matching the JBD fuzzy optimization problem into cumulant maximization problem, has a clear physical meaning of the expression, show that the matching problem can be achieved through cross cumulant maximization group signal. This method does not need to artificially set the threshold, can guarantee the convergence of the proposed.5. A blind modulation recognition algorithm for MIMO-STBC system based on maximum likelihood. The modulation type recognition is an important research topic in communication parameter estimation, but for non cooperative MIMO-S Blind modulation recognition system TBC is rarely reported. This paper first indicates that the MIMO-STBC system with ICA model, and based on this model a modulation classifier based on maximum likelihood; then according to the independent source signal modulation type is divided into independent and independent group constellation; then according to the two estimation methods are discussed for Virtual Constellation the channel matrix based on MICA; finally eliminate some fuzzy, fuzzy modulation classifier is not sensitive to the residual, does not affect the modulation classification results. The algorithm is applicable to non cooperative MIMO-STBC system, and has good recognition performance.

【学位授予单位】：电子科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN919.3
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本文编号：1742314