發(fā)布時間：2018-04-13 01:24

  本文選題：多輸入多輸出 + 空時分組碼��； 參考：《電子科技大學(xué)》2014年博士論文

【摘要】：近年來,隨著多媒體技術(shù)和Internet的發(fā)展,人們對無線通信的質(zhì)量和通信速率提出了越來越高的要求。多輸入多輸出(Multiple Input Multiple Output,MIMO)技術(shù)可以有效地提高信道容量,是這些問題的有效解決方案�？諘r編碼(Space-Time Coding,STC)在發(fā)射的時域信號中引入編碼冗余而得到分集增益,而空時分組碼(Space-Time Block Code,STBC)是空時編碼的主要類型。作為下一代無線通信的關(guān)鍵技術(shù)之一,IEEE 802.11n和IEEE 802.16e已經(jīng)對MIMO-STBC通信系統(tǒng)制定了一系列的標(biāo)準(zhǔn)。通信系統(tǒng)參數(shù)盲估計廣泛應(yīng)用于軍事及民用通信系統(tǒng)中,是一個重要的研究領(lǐng)域。目前對合作MIMO-STBC通信系統(tǒng)的研究較多,而很少有文獻(xiàn)從非合作角度對MIMO-STBC系統(tǒng)進(jìn)行參數(shù)估計。獨立分量分析(Independent Component Analysis,ICA)是上個世紀(jì)九十年代發(fā)展起來的一種重要的信號處理方法,不像其他方法,獨立分量分析不需要很強(qiáng)的先驗假設(shè),因此是非合作信號處理的有力工具之一。本文以ICA算法為工具,從非合作角度對MIMO-STBC系統(tǒng)參數(shù)盲估計進(jìn)行了研究,主要成果有:1、提出了一種適用于空間色噪聲環(huán)境的信源數(shù)目估計算法。信源個數(shù)是MIMO-STBC系統(tǒng)的一個重要參數(shù),文獻(xiàn)中的經(jīng)典算法大多假設(shè)噪聲在空間上不相關(guān),然而對于受干擾的MIMO信道通常假設(shè)噪聲在空間相關(guān),所以這些算法不能適用于這種場景。本文利用接收信號的高階累積量矩陣的對角特性,通過特征值分解估計出噪聲子空間,經(jīng)過酉變換后,對應(yīng)于噪聲子空間的信號分量服從高斯分布。本文以峭度為統(tǒng)計量,通過假設(shè)檢驗來檢測信號的高斯性,從而估計出信源數(shù)目。該方法適用于空間色噪聲,具有較好的檢測性能。2、研究了空時分組碼在高維特征空間中的分類識別問題。本文利用空時分組碼的循環(huán)平穩(wěn)特性,對接收到的信號在多個時延上做自相關(guān),對于不同的空時分組碼,這些自相關(guān)矩陣的二范數(shù)具有不同的峰值和谷點,所以這些峰值和谷點攜帶了編碼類型的信息,可以作為特征來區(qū)分各種空時分組碼。為了更好地利用這些信息,本文將其映射到高維特征空間對空時分組碼進(jìn)行分類。由于利用了更多的信息,所以基于高維特征空間的識別算法的性能優(yōu)于一維識別算法。3、提出了一種旋轉(zhuǎn)的多天線空時盲分離算法。在合作通信中,由于編碼信息已知,可以通過估計信道矩陣來分離接收到的信號。而在非合作系統(tǒng)中,在編碼矩陣和信道狀態(tài)信息(Channel State Information,CSI)都未知時如何分離接收到的信號是一個具有挑戰(zhàn)性的問題。本文提出了一個旋轉(zhuǎn)的MIMO-STBC通信模型,該模型將編碼矩陣和信道矩陣合并為虛擬信道矩陣(Virtual Channel Matrix,VCM),通過對接收信號旋轉(zhuǎn)一定角度來最大化源信號的獨立性,使其滿足ICA算法的假設(shè)。在此模型上應(yīng)用ICA算法估計的信號反向旋轉(zhuǎn)相應(yīng)角度后就可以分離出源信號。該方法無需在發(fā)射端進(jìn)行預(yù)編碼,并且不需要編碼信息。4、研究了多維獨立分量分析(Multidimensional Independent Component Analysis,MICA)算法在非合作MIMO-STBC系統(tǒng)中的應(yīng)用,并提出了一種基于高階統(tǒng)計量(Higher-Order Statistics,HOS)的MICA算法。為了分離接收到的信號,本文用ICA模型來表示MIMO-STBC系統(tǒng),然而對一些調(diào)制信號,由于源信號成組獨立而不能滿足ICA算法的獨立性假設(shè)。針對這種情況,本文采用MICA算法來分離接收到的信號,并利用接收信號的高階累積量矩陣成塊對角的特性提出了一種MICA算法,該算法通過對四階累積量矩陣聯(lián)合塊對角化(Joint Block-Diagonalization,JBD)估計出混合矩陣。針對文獻(xiàn)中基于一步優(yōu)化的JBD算法收斂性差的問題,本文采用兩步優(yōu)化法求解JBD問題:首先對四階累積量矩陣進(jìn)行聯(lián)合對角化(Joint Diagonalization,JD),然后求解分離信號的配對模糊。為了去除信號的配對模糊,本文將JBD優(yōu)化問題轉(zhuǎn)化為互累積量的極大化問題,該表達(dá)式具有明確的物理意義,表明配對問題可以通過極大化組內(nèi)信號的互累積量來實現(xiàn)。該方法不需要人為設(shè)置門限,可以保證收斂性。5、提出了一種基于最大似然的MIMO-STBC系統(tǒng)的盲調(diào)制識別算法。調(diào)制類型識別是通信參數(shù)估計的一個重要研究課題,然而對非合作MIMO-STBC系統(tǒng)的盲調(diào)制識別卻鮮有報道。本文首先用ICA模型來表示MIMO-STBC系統(tǒng),并根據(jù)此模型提出一個基于最大似然的調(diào)制分類器;然后根據(jù)源信號的獨立性將調(diào)制類型分為獨立和成組獨立星座;然后分別針對這兩種星座討論了基于MICA的虛擬信道矩陣估計方法;最后在消除了部分模糊后,調(diào)制分類器對剩余模糊不敏感,不影響調(diào)制分類結(jié)果。該算法適用于非合作MIMO-STBC系統(tǒng),并且有較好的識別性能。
[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.

【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN919.3
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本文編號：1742314