一种不受信道阶数估计影响的SIMO直接盲均衡算法

针对单输入多输出（Single-Input-Multiple-Output, SIMO）模型提出一种完全不需要信道阶数估计的直接盲均衡算法。文章利用接收数据的截短协方差矩阵和信号子空间的关系设计一种零延迟均衡器,并通过信道矩阵和均衡器系数的合响应特性克服了算法相位偏转的问题,最后得到一种对信道阶数估计鲁棒并且没有相位偏转的盲均衡算法。该算法不同于一般子空间类算法,不需要直接对接收信号的协方差矩阵进行信号子空间和噪声子空间的分解,因此对信道阶数估计具有很强的鲁棒性。文章给出了算法的Batch实现过程,同时为更好适应一般时变信道环境和实现实时处理的要求,通过递归迭代得到算法的自适应实现过程。仿真实验表明该算法几乎不受信道阶数过估计或欠估计的影响,同时该算法具有良好的均方误差（Mean Square Error, MSE）和误符号率SER（Symbol Error Rate, SER）性能,并且具有很快的收敛速度。      

基于子空间投影的信道阶数盲估计算法

根据联合阶数估计最小二乘平滑算法(J-LSS)中投影误差矩阵的特点,利用其零空间向量形成的特殊矩阵的秩与信道阶数的关系,分别构造2个阶数估计代价函数。将2个代价函数归一化后联合构建成新的代价函数,新的代价函数较使用单一代价函数提升了在低信噪比下的辨识率。仿真结果表明,与传统算法相比,该算法在较低的信噪比和小样本观测数据条件下,有很好的估计性能。     

基于均衡代价函数的信道阶数盲估计算法

针对信道阶数估计问题,利用单输入多输出(Single-Input Multiple-Output,SIMO)有限冲激响应(Finite Impulse Response,FIR)信道的结构特点和输入/输出信号的统计特征,提出了一种基于均衡代价函数的信道阶数盲估计算法.首先计算了归一化最小二乘均衡(Normalized Least Squares Equalization,NLSE)代价函数在理想条件下的理论渐近值,并指出其拐点与信道阶数之间的对应关系.然后分析了NLSE代价函数在实际条件下的近似值.最后引入了拐点优化因子,提出了一种基于NLSE代价函数拐点检测的信道阶数估计算法.理论分析和仿真结果表明,在信噪比(Signal-to-Noise Ratio,SNR)较低和信道首尾系数较小的情况下,该算法比现有其它方法具有更强的鲁棒性,可以获得更小的接收信号均衡误差.     

SIMO信道中基于奇异值分解的盲信噪比估计算法

信噪比（SNR）是现代通信信号处理中一个重要参数,许多算法需要它作为先验信息以获取最佳估计性能。针对单输入多输出（SIMO）系统的信噪比估计问题,本文提出了一种盲信噪比估计算法。该算法利用多路信号协方差矩阵的奇异值分解（SVD）,通过计算矩阵的最大特征值实现各路信号信噪比估计。该算法无需知道信号的先验信息,能够对加性高斯白噪声信道（AWGN）和多径信道下常用的数字调制信号进行信噪比估计。仿真结果表明该算法具有良好的估计性能。与单路信号中基于SVD信噪比估计算法相比,该算法无需估计信号空间与噪声空间维数,提高了估计精度,同时大大减小计算复杂度。      

OFDM系统中信道有效阶数的估计

信道估计是无线通信的关键技术之一，知道信道长度可以提高信道估计的精度．本文针对基于OFDM调制技术的无线通信系统中LS信道估计算法时域结果的构成特点，结合无线信道冲激响应在时域为有限冲激响应的特点，提出对LS信道估计算法的时域结果进行能量检测，并进行逆向搜索以获得信道有效阶数估计的算法．同时从检测概率角度给出了检测门限．最后给出了计算机仿真结果．理论分析和计算机仿真表明此算法能够很好地得到信道有效阶数估计结果．     

水声稀疏时变SIMO信道盲均衡算法

基于复指数基扩展模型（Complex exponential basis expansion model, CE-BEM）,利用信道的稀疏特性和发送信号的常模特性（Constant Modulus, CM）,提出水声稀疏时变（Time-variant, TV）SIMO信道盲均衡算法。首先采用l0-范数约束的比例系数归一化最小均方误差常模算法对等效信道矩阵的稀疏时不变部分进行均衡,然后采用基频率估计算法估计基频率并对多普勒频移进行补偿,最后对恢复信号中存在的相偏进行估计补偿。仿真实验结果表明,本文算法提高了均衡器的收敛速度,降低了剩余码间干扰。      

无线OFDM系统中信道冲激响应有效阶数估计

在基于OFDM技术的无线通信系统中,针对LS信道估计算法时域结果的构成,同时结合无线信道冲激响应在时域为有限冲激响应的特点,提出对LS信道估计算法的时域结果进行能量检测,并实行逆向搜索以获得信道有效阶数估计值的算法。理论分析和计算机仿真表明此算法能够得到很好的无线信道有效阶数估计结果。     

MIMO-OFDM系统中的信道盲估计技术

MIMO-OFDM能极大提高系统的容量,同时能克服频率选择信道对于信号的影响.文中讨论了MIMO-OFDM信道估计中能解决带宽有效利用率的信道盲估计技术.在虚载波可用的情况下,信道盲估计可以不用循环前缀来提高信道利用率,避免由于插入导频符号而带来的资源浪费.通过对一个二传送、三接收天线系统的仿真结果表明,文中提出的噪声子空间方法,能有效地减少信道估计错误,使系统容量达到更高信道利用率.     

含共零点SIMO信道直接盲序列检测与信道辨识

在ε均衡概念基础上，提出了对含公零点SIMO信道的盲辨识算法。该算法充分利用发送符号属于有限字符集的先验知识，先直接盲检测发送序列，然后再进行信道辨识。仿真结果表明：不管信道是否包含公零点，本文提出的信道盲辨识ε算法性能都明显地优于基于二阶统计量的其它经典算法。     

MC V-BLAST系统的MIMO信道盲估计

本文研究多载波垂直分层空时(MC V-BLAST)系统的下行频率选择性衰落多输入多输出(MIMO)无线信道估计问题.本文首先为MC V-BLAST系统提出了一种新颖的移不变性编码方法.利用上述移不变性性质,本文进一步提出了相应的下行频率选择性衰落MIMO无线信道的盲估计方法.仿真结果表明了本文移不变性编码方法的有效性和信道盲估计方法的性能.     

总体最小二乘拟合的盖氏圆盘信源数估计法

针对调整因子设置不当造成在信源数估计时盖氏圆盘法（GDE）性能下降的问题,提出一种基于总体最小二乘拟合的盖氏圆盘法（TLS-GDE）。该方法以圆盘半径作为拟合点进行直线拟合,若拟合点含信号圆盘半径,则拟合偏差较大,利用这一特性制定了比值阶跃准则,进行信源数判决,解决了盖氏圆盘法对调整因子依赖的问题。最后,仿真结果表明,该方法鲁棒性较好,在低信噪比下性能要优于GDE算法,更具有实用价值。      

Channel Estimation by Using Short Training Sequences in CDMA Systems

Multiuser detection techniques are known to be effective to counter the presence of multiuser interference in code division multiple access channels. Multiuser detectors can provide excellent performance only when the channel impulse responses of all the users are precisely known. Hence, channel estimation becomes a challenging issue in mobile communication systems. In this paper, we address the problem of efficient maximum likelihood mobile radio channel estimation at high channel efficiency that requires a short training sequence along with the known spreading sequence. The proposed system can be employed in both the uplink and downlink of a heavily loaded multiuser CDMA system. The extension of the approach with unknown users' delays are also proposed. We present results that show the success of this method in recovering the transmitted bits with a relatively small number of preamble bits. Ahmet Rizaner was born in Larnaca, Cyprus, on January 31, 1974. He received the B.S. and M.S. degrees in Electrical and Electronics Engineering from the Eastern Mediterranean University, Famagusta, North Cyprus, in 1996 and 1998, respectively. He completed his PhD. degree in Electrical and Electronic Engineering in Eastern Mediterranean University and joined Eastern Mediterranean University as a lecturer in 2004. He is lecturing in the School of Computing and Technology. His main research interests include CDMA communications, adaptive channel estimation, and multiuser detection techniques. Hasan Amca was born in 1961 in Nicosia-Cyprus. He graduated from the Higher Technological Institute in Magosa-Cyprus (which is renamed later as Eastern Mediterranean University). He joined EMU in 1985 after receiving a M.Sc. (Digital Signal Processing) degree from the University of Essex in England (1985). He took his Ph.D. (Mobile Communications) from the University of Bradford where he was on a Commonwealth scholarship. He has been teaching in the Electrical and Electronic Engineering Department of Eastern Mediterranean University since 1993 where he also served as the vice chairman from Spring 1998 to Spring 2000. He has been appointed as the Director of the School of Computing and Technology of the EMU since Spring 2000. His research interests include Multi User Detection of CDMA signals, Adaptive Equalisation, Multi Carrier Systems, Mobile Radio Systems and Networks, Internet and Information Technology Applications in Education. Kadri Hacıoğlu was born in Nicosia, Cyprus. He received the B.Sc., M.Sc., and Ph.D. degrees in electrical and electronic engineering from the Middle East Technical University, Ankara, Turkey, in 1980, 1984, and 1990, respectively. After his two-year military service, in 1992, he joined the faculty of Eastern Mediterranean University, Magosa, North Cyprus, as an Assistant Professor, and became an Associate Professor in 1997. While there, he taught several classes on electronics, digital communications, speech processing and neural networks. During this time, he conducted research on applying fuzzy logic, neural networks, and genetic algorithms to signal processing and communications problems. From 1998 to 2000, he was a Visiting Professor in the Department of Computer Science, University of Colorado, Boulder. Here, he taught classes on neural networks and continued his research. Since 2000, he has been a Research Associate at the Center for Spoken Language Research, University of Colorado. He has authored or coauthored numerous papers and supervised a dozen M.Sc./Ph.D. theses. His current research interests are concept-based language modeling, speech understanding, natural language generation, and search methods in speech recognition/understanding. He also does research on multiuser detection and equalization in CDMA systems. Ali Hakan Ulusoy was born in Eskişehir, Turkey, on June 3, 1974. He graduated from the double major program of the department of Electrical and Electronic Engineering and department of Physics in Eastern Mediterranean University as the first rank student of Faculty of Engineering in 1996. He received his M.S. degree in Electrical and Electronic Engineering in Eastern Mediterranean University in 1998. He completed his PhD. degree in Electrical and Electronic Engineering in Eastern Mediterranean University and joined Eastern Mediterranean University as a lecturer in 2004. He is lecturing in the School of Computing and Technology. His current research interests include receiver design, multi-user detection techniques, blind and trained channel estimation in Code Division Multiple Access (CDMA).     

Channel Estimation in Massive MIMO Systems Using a Modified Bayes-GMM Method

Wireless Personal Communications - In this paper, we study the uplink channel estimation based on machine learning algorithm in massive MIMO systems. Based on the sparsity of channel gains in the...     

Adaptive Channel Estimation Using Pilot-Embedded Data-Bearing Approach for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems employing coherent receivers crucially require channel state information (CSI). Since the multipath delay profile of channels is arbitrary in the MIMO-OFDM systems, an effective channel estimator is needed. In this paper, we first develop a pilot-embedded data-bearing (PEDB) approach for joint channel estimation and data detection, in which PEDB least-square (LS) channel estimator and maximum-likelihood (ML) data detection are employed. Then, we propose an LS fast Fourier transform (FFT)-based channel estimator by employing the concept of FFT-based channel estimation to improve the PEDB-LS one via choosing a certain number of significant taps for constructing a channel frequency response. The effects of model mismatch error inherent in the proposed LS FFT-based estimator when considering noninteger multipath delay profiles and its performance analysis are investigated. The relationship between the mean-squared error (MSE) and the number of chosen significant taps is revealed, and hence, the optimal criterion for obtaining the optimum number of significant taps is explored. Under the framework of pilot embedding, we further propose an adaptive LS FFT-based channel estimator employing the optimum number of significant taps to compensate the model mismatch error as well as minimize the corresponding noise effect. Simulation results reveal that the adaptive LS FFT-based estimator is superior to the LS FFT-based and PEDB-LS estimators under quasi-static channels or low Doppler's shift regimes     

Bounds on SIMO and MIMO Channel Estimation and Equalization with Side Information

Constrained Cramér-Rao bounds are developed for convolutive multi-input multi-output (MIMO) channel and source estimation in additive Gaussian noise. Properties of the MIMO Fisher information matrix (FIM) are studied, and we develop the maximum rank of the unconstrained FIM and provide necessary conditions for the FIM to achieve full rank. Equality constraints on channel and signal parameters provide a means to study the potential value of side information, such as training symbols (semi-blind case), constant modulus (CM) sources, or known channels. Nonredundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating the performance of SIMO and MIMO channel estimation and equalization algorithms. We present examples using the constant modulus blind equalization algorithm. The constrained bounds are also useful for evaluating the relative value of different types of side information, and we present examples comparing semi-blind, constant modulus, and known channel constraints. While the examples presented are primarily in the communications context, the CRB framework applies generally to convolutive source separation problems.     

OFDM系统中的盲信道估计

本文从OFDM信号的矩阵表示出发,分析比较了OFDM系统中现有的各种盲信道估计方法。OFDM盲信道估计方法分为两类,一类是统计型方法,它利用了发送信号和接收信号的统计特性;另一类是确定型方法,它利用了发送调制信号的固有特性。一般而言,统计型方法的计算量较小,但是估计精度不高且估计的实时性不好;而确定型方法的估计精度较高,实时性较好,但是其计算量较大。计算机仿真表明,这些盲信道估计方法的性能受信道参数尤其是多普勒频率影响很大,盲信道估计的实用化有待进一步研究。     

Time-varying Channel Estimation and Symbol Detection Using Superimposed Training in OFDM Systems

In mobile orthogonal frequency division multiplexing (OFDM) systems, time-varying channels result in severe intercarrier interference (ICI), and greatly degrade the system performance. So, it is necessary to estimate the accurate channel for equalization of received symbols. But, the conventional pilot-assisted channel estimation scheme consumes valuable bandwidth. In this paper, we adopt superimposed training approach for OFDM systems to estimate the time-varying channel, which is approximated by a basis expansion model (BEM). The proposed scheme is an extension of the superimposed training approach previously proposed for time-invariant channels in OFDM systems. At the same time, we employ an iterative best linear unbiased estimator (BLUE) to minimize the mean square error (MSE) of the coefficient estimates and improve the system performance. Simulation results prove the effectiveness of the proposed scheme in fast time-varying scenario.

New Channel Estimation Method Using Singular Spectrum Analysis for OFDM Systems

Wireless Personal Communications - This paper presents a new method for OFDM channel estimation (CE) using singular spectrum analysis (SSA). In this method, the conventional LMMSE procedure is used...