New Blind Block Synchronization for Transceivers Using Redundant Precoders

This paper studies the blind block synchronization problem in block transmission systems using linear redundant precoders (LRP). Two commonly used LRP systems, namely, zero padding (ZP) and cyclic prefix (CP) systems, are considered in this paper. In particular, the block synchronization problem in CP systems is a broader version of timing synchronization problem in the popular orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithms exploit the rank deficiency property of the matrix composed of received blocks when the block synchronization is perfect and use a parameter called repetition index which can be chosen as any positive integer. Theoretical results suggest advantages in blind block synchronization performances when using a large repetition index. Furthermore, unlike previously reported algorithms, which require a large amount of received data, the proposed methods, with properly chosen repetition indices, guarantee correct block synchronization in absence of noise using only two received blocks in ZP systems and three in CP systems. Computer simulations are conducted to evaluate the performances of the proposed algorithms and compare them with previously reported algorithms. Simulation results not only verify the capability of the proposed algorithms to work with limited received data but also show significant improvements in the block synchronization error rate performance of the proposed algorithms over previously reported algorithms.      

循环前缀块传输系统中的一种MIMO盲信道估计算法

信道估计一直是无线通信领域的研究热点之一,信道参数估计的好坏对系统的整体性能有着至关重要的影响。针对采用循环前缀的多输入多输出(MIMO-CP)系统,本文提出了一种基于子空间的盲信道估计方法,该算法利用了循环前缀所引起的冗余信息。基于子空间的盲信道估计算法都是通过对接收块的自相关矩阵进行奇异值分解(SVD)来实现信道估计的,因此需要利用尽可能多的接收块来得到准确的自相关矩阵的估计值,这就意味着会产生过久的判决延迟以及不能准确对快变信道进行跟踪。利用MIMO-CP系统中系统矩阵特有的块循环特性,对于连续的两个接收数据块以及对应的循环前缀部分组成的向量,可以重新构造一组新的向量而不改变系统的信道矩阵,因此可以通过较少的接收块来得到准确的自相关矩阵的估计值,该方法十分适用于对快变信道的盲估计。文章通过仿真分析了在不同的重复系数以及不同的接收块下该算法的性能并且比较了该算法与现有的“预编码”、“虚拟子载波”等盲信道估计算法的性能。仿真结果表明,提出的算法利用较少的数据块个数就得到了一个可靠的信道估计值和很好的误码率性能。      

采用奇异值分解方法的半盲信道辨识算法

针对全盲信道辨识算法无法辨识含公零点信道且对信道阶数误差敏感的问题,提出一种半盲信道辨识算法。通过奇异值分解将信道矩阵分解为同维矩阵与酉矩阵乘积的形式,分别利用接收数据和已知符号求解同维矩阵与酉矩阵,最终得到信道矩阵的闭式解。该算法有效地克服了全盲信道辨识算法的诸多局限性,避免了传统半盲方法面临的最优加权选择问题,性能稳定,且对信道噪声与信道阶数都具有较强的鲁棒性。仿真实验验证了所提算法的有效性与优越性。     

Blind Channel Identification for Cyclic-Prefixed MIMO-OFDM Systems with Virtual Carriers

This paper applies the repetition index scheme(RIS)to the channel identification of cyclic prefixed(CP)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)systems with virtual carriers(VCs)in the environment of the number of receive antennas being no less than that of transmit antennas.The VCs will cause a rank deficiency problem in computing the subspace information.With the subcarrier mapping matrix,the received signal is simplified to remove the rank deficiency.We use the RIS scheme to generate many times of equivalent symbols so the channel identification can converge with few received OFDM blocks.The RIS scheme will convert the white noise into non-white noise.With the Cholesky factorization,a noise whitening technique is developed to turn the non-white noise back to white noise.We further analyze the necessary conditions of identifiability of channel estimation.Simulations are performed to show the superiority of the proposed method.     

一种基于信道盲辨识和盲均衡的多径信道调制方式识别算法

针对多径环境下MPSK和MQAM信号的调制识别问题,该文提出了一种基于信道盲辨识和盲均衡的调制识别算法。算法首先进行信道阶数精确估计,然后以矩阵外积分解为基础,加入特征值扰动分析,提高了信道盲辨识和盲均衡的稳定性,最后,对均衡后的星座图进行多级减法聚类,得到信号的调制方式。与现有算法相比,算法结构简单,所需数据量小,适用范围广,实用性强。仿真结果证明了算法的有效性。     

Precoder design and blind identification of MIMO channels

In this paper, blind identification problem based on second-order statistics (SOS) of received signals is investigated for multiple-input multiple-output (MIMO) channels. It is shown that precoders introduced in transmitters can provide certain extra information for blind identification problem and commonly used identifiability conditions on MIMO channels can be relaxed. A design criterion for the precoders in frequency domain is proposed. Two blind identification algorithms based on the knowledge of the precoders and SOS of the received signals are then developed. In these algorithms, due to particular structure of the precoders, the blind identification problem for a MIMO channel is decomposed into several independent single-input multiple-output (SIMO) channel identification problems. The presented algorithms are robust to an overestimated channel order and not sensitive to signal-to-noise ratio (SNR). Compared with the existing results, a larger class of MIMO finite-impulse-response (FIR) channels can be identified by the new methods.     

OFDM系统中基于恒模的盲信道估计算法

该文提出了一种适用于采用PSK调制方式的正交频分复用(OFDM)系统的准盲信道估计算法。该算法首先利用系统信号的恒模特性得到有限个可能的信道,然后利用信号的有限字符特性从可能信道中寻找出最佳信道,因而具有较低的计算复杂度。与现有的盲信道估计算法不同,该算法利用二阶统计量而不是高阶统计量估计信道,获得了较好的估计性能。仿真结果表明,该算法的性能优于基于有限字符特性的盲信道估计算法。     

Group-blind intersymbol multiuser detection for downlink CDMA with multipath

Group-blind multiuser detectors for uplink code-division multiple-access (CDMA) were recently developed by Wang and Host-Madsen. These detectors make use of the spreading sequences of known users to construct a group constraint to suppress the intracell interference. However, such techniques demand the estimation of the multipath channels and the delays of the known users. In this paper, several improved blind linear detectors are developed for CDMA in fading multipath channels. The proposed detectors utilize the correlation information between consecutively received signals to generate the corresponding group constraint. It is shown that by incorporating this group constraint, the proposed detectors can provide different performance gains in both uplink and downlink environments. Compared with the previously reported group-blind detectors, our new methods only need to estimate the multipath channel of the desired user and do not require the channel estimation of other users. Simulation results demonstrate that the proposed detectors outperform the conventional blind linear multiuser detectors.     

A subspace blind channel estimation method for OFDM systems without cyclic prefix

We propose a subspace based blind channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems over a time-dispersive channel. Our approach is motivated by the resemblance of the multichannel signal model resulting from oversampling (or use of multiple receive sensors) of the received OFDM signal to that in conventional single carrier system. The proposed algorithm distinguishes itself from many previously reported channel estimation methods by the elimination of the cyclic prefix, thereby leading to higher channel utilization. Comparison of the proposed method with other two reported subspace channel estimation methods is presented by computer simulations to support its effectiveness.     

Blind channel estimation based on maximizing the eigenvalue spread of cumulant matrices in (2 × 1) Alamouti's coding schemes

The popular Alamouti orthogonal space time code attains full transmit diversity in multiple antenna systems. This paper addresses the problem of blind channel identification in (2 × 1) Alamouti coded systems. Under the assumption of independent symbol substreams, the channel can be estimated from the eigendecomposition of matrices composed of second‐ or higher‐order statistics (cumulants) of the received signal. The so‐called joint approximate diagonalization of eigenmatrices (JADE) method for blind source separation via independent component analysis is optimal in that it tries to simultaneously diagonalize a full set of fourth‐order cumulant matrices. To reduce computational complexity, we perform the eigenvalue decomposition of a single cumulant matrix, which is judiciously chosen by maximizing its expected eigenvalue spread. Simulation results show that the resulting technique outperforms existing blind Alamouti channel estimation methods and achieves a performance close to JADE's at a fraction of the computational cost. Copyright © 2010 John Wiley & Sons, Ltd.     

基于子空间分解的快速盲均衡解调算法

基于高阶统计的盲均衡算法需要大量的观测数据,当观测数据有限或信道变化较快时,由于模型失配将使均衡性能严重下降,因此短数据、快收敛是近年来盲均衡技术研究的主要方向之一。本文讨论了基于子空间分解的信道识别和盲均衡技术,利用卡尔曼滤波方程,给出了一种快速收敛的盲均衡解调算法,算法只需要较少的数据样本,仿真结果表明该方法是有效的。     

基于子空间分解的OFDM信道盲辨识

该文提出一种基于子空间分解的正交频分复用(OFDM)信道的盲辨识算法,将OFDM信号等效为单输入多输出的过采样信号,采用过采样信号的循环稳态特性和子空间分解方法估计信道参数,算法不需要任何训练序列和周期性的引导信号,实现了0FDM信道的盲辨识。对于宽带OFDM移动通信系统,通常子信道数较大,信道响应持续时间短于0FDM符号周期,因此,可以将整个系统分为若干个子系统,各子系统分别进行信道辨识,能有效地降低信道估算的复杂性。     

一种提高数据利用效率的单载波信道估计方法

在单载波频域均衡系统中,提出了一种高效利用已知接收数据进行子空间盲信道估计的方法。对每个接收数据块内的符号进行循环移位,将原一列数据向量扩展为两列行线性非相关数据矩阵。因此,在相同均衡性能指标下,所利用的接收数据量是以往使用的子空间盲信道估计方法的一半。这意味着,此方案适用于某些对数据量有严格限制要求的场合。仿真实验结果表明,在保证性能无衰减的前提条件下,节省一半的所需数据量。     

Efficient channel blind estimator for multiuser system with conjugate cyclostationarity

The problem of blind channel identification in a multiuser system is considered in this article. For this purpose, a blind identification algorithm is proposed based on the conjugate cyclostationarity of the received signal. The new approach contains a two-stage identification procedure. First, the separation technique in the cyclic domain is used to separate the second-order cyclic statistics for each user. Second, a subspace algorithm based on the rational subspace theory is exploited to estimate the desired channel. Theoretical analysis and simulation results show that this algorithm is suitable for a multiuser system. Compared with other methods, the algorithm shows good performance even in a bad situation when the number of users is large and the diversity condition is unavailable.     

Kalman滤波下的多信号单通道盲源分离

窄带物联网环境中,接收机收到的信号通常为多路混合信号,对单通道接收来说,利用常规盲源分离方法很难实现混合信号的分离和源信号提取。针对这一问题,本文提出了一种利用Kalman滤波算法进行信号估计,解决单通道盲源分离的方法。该方法利用信号间的时序结构,通过Kalman滤波算法对多信号混合中的源信号不断估计并迭代更新,最终得到分离信号。仿真实验结果表明,该方法能有效估计并分离出源信号。      

基于粒子滤波器的盲辨识和盲均衡新方法

提出了一种新的基于粒子滤波器的盲辨识及盲均衡算法。在对信道进行辨识时，通过对信道均值的采样来代替对真实信道的采样，避免了对信道的后验密度进行采样，从而降低了算法的复杂度。算法还采用先验密度作为重要性函数，以便于对重要性函数进行采样。仿真结果表明，该算法收敛速度快，所需的数据量少，在信噪比较低时也能完成对信道的盲辨识和盲均衡。     

Column-anchored zeroforcing blind equalization for multiuserwireless FIR channels

We propose a direct blind zeroforcing approach to cancel intersymbol interference (ISI) in multiple user finite impulse response (FIR) channels. By selectively anchoring columns of the channel convolution matrix, we present two column-anchored zeroforcing equalizers (CAZE), one without output delay and one with a chosen delay. Unlike many known blind identification algorithms, these equalizers do not need an accurate estimate of the channel orders. Exploiting second-order statistics (SOS) of the received signals, they can retain preselected d columns in the channel convolution matrix (d is the number of users) and force the remaining columns to zero. CAZE can effectively equalize single-input-multiple-output (SIMO) systems and can reduce dynamic multiple-input-multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. Simulation results show that the CAZE is not only effective for blind equalization of linear quadrature amplitude modulation (QAM) systems, but it is also applicable to the nonlinear GMSK modulation in the popular wireless GSM systems when computational cost severely limits the use of nonlinear methods such as the Viterbi algorithm     

CDMA时变色散信道基于一阶统计量的盲辨识算法

在深入分析CDMA时变色散信道的离散正则模型的基础上,给出了一种新的CDMA时变色散信道的盲辨识方法.使用解相关接收机分离出时变有效特征波形中的信道参数后,仅利用处理后信号的一阶统计量,通过少量的导频符号,就可以实现对信道的辨识.这种方法和通常的基于二阶统计量的盲辨识算法相比具有更低的运算复杂度,同时所需要的观察数据也更少.仿真结果证明了算法的有效性.     

Estimation and equalization of doubly selective channels using known symbol padding

This paper considers the situation where users that experience high-mobility transmit data over frequency-selective channels, resulting in a doubly selective channel model (i.e., time- and frequency-selective channels) and this within the framework of Known Symbol Padding (KSP) transmission. KSP is a recently proposed block transmission technique where short sequences of known symbols acting as guard bands are inserted between successive blocks of data symbols. This paper proposes three novel channel estimation methods that allow for an accurate estimation of the time-varying transmission channel solely relying on the knowledge of the redundant symbols introduced by the KSP transmission scheme. The first method is a direct adaptive one while the others rely on a recently proposed model, the Basis Expansion Model (BEM), where the doubly selective channel is approximated with high accuracy using a limited number of complex exponentials. An important characteristic of the proposed methods is that they exploit all the received symbols that contain contributions from the training sequences and blindly filter out the contribution of the unknown surrounding data symbols. Besides these channel identification methods, the classical KSP equalizers are adapted to the context of doubly selective channels, which allows evaluation of the bit-error-rate (BER) performance of a KSP transmission system relying on the proposed channel estimation methods in the context of doubly selective channels. Simulation results show that KSP transmission is indeed a suitable transmission technique toward the delivery of high data rates to users experiencing a high mobility, when adapted KSP equalizers are used in combination with the proposed channel estimation methods.     

The application of blind channel identification techniques toprestack seismic deconvolution

One objective of seismic signal processing is to identify the layered subsurface structure by sending seismic wavelets into the ground. This is a blind deconvolution process since the seismic wavelets are usually not measurable and therefore, the subsurface face layers are identified only by the reflected seismic signals. Conventional methods often approach this problem by making assumptions about the subsurface structures and/or the seismic wavelets. In this paper an alternative technique is presented. It applies blind channel identification methods to prestack seismic deconvolution. A unique feature of this proposed method is that no such assumptions are needed. In addition, it fits into the structure of current seismic data acquisition techniques, thus no extra cost is involved. Simulations on both synthetic and field seismic data demonstrate that it is a promising new method for seismic signal processing