一种基于子空间分解的IIR信道盲辨识算法

本文证明了对IIR信道输出进行过采样（采样率是输入码率的整数倍）可以转化成单输入多输出的多信道模型，并且不同的信道有相同的AR系数。基于这一特性本文提出一种基于子空间分解的信道参数盲辨识方法，即不同信道的MA系数可以输出信号的噪声子空间唯一确定，而AR系数则可以通过求解YW方程得到。     

一种基于多信道模型的IIR信道盲辩识算法

本文的主要贡献在于:1)证明了对单输入单输出(Single-Input-Single-Output SISO)IIR信道的输出进行过采样可以将其转化成单输入多输出(Single-Input-Multiple-Output SIMO)的多信道模型,不同的子信道具有相同的AR系数。2)由SISO向SIMO模型的转化时,本文指出子信道数是可变的,并且当子信道数等于过采样的倍数时,辨识效果最好。实验证明本文算法估计效果好,计算量小。特别是当用于估计的点数较少时仍能保持良好的性能,因而可适用于快变信道。     

基于时延分集的MIMO多径信道盲估计算法

发送时延分集在空时编码中是一种有效可行的方法。本文提出了一种基于发送时延分集的多径信道估计方法,指出如果发送时延大于信道时延,多输入多输出（MIMO）信道可以转化为特殊的单输入多输出信道,通过对信道输出进行子空间分解可以估计出信道参数。仿真结果显示了本文算法的有效性。     

空时编码多载波码分多址MIMO系统下行频率选择性信道盲估计

基于子空间方法的无线信道盲估计由于其算法的固有特性,使得估计结果与实际信道之间存在一个不确定复系数,无法得到无线信道的精确估计。在利用子空间分解方法对空时编码多输入多输出MC-CDMA系统下行频率选择性信道盲估计的基础上,利用发射符号的有限码集特性,将单载波系统下的模糊复系数盲辨识方法推广到多载波多输入多输出系统,从而得到信道的精确估计。Monte-Carlo仿真表明,在信噪比较低的情况下,本方法的信道估计误差仍然较小。     

TD-SCDMA HSPA+中的单双流MIMO技术的研究

多输入多输出(MIMO)技术是近年来移动通信的热门研究领域,它的特征在于无线发射机和接收机都引入了多根天线.MIMO技术除了通过空间分集方式获得容量的提升外,还可以通过空间复用方式,利用不同天线信道的独立性建立多个空间子信道来提高容量.但是,无论是基于空间分集的单流模式还是基于空间复用的双流模式,单独在TD-SCDMA...     

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。      

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。     

具有周期调制和循环前缀的块传输系统中MIMO信道的盲辨识

在块传输系统中,可通过添加块前缀来辅助信道识别。一种针对具有循环前缀和周期调制的块传输系统的盲辨识方法,利用了信道矩阵的块循环特性来求解信道系数。该文将该算法推广到了多用户系统,并给出了算法的辨识条件。同样是基于二阶统计量的方法,新算法克服了基于子空间分解类算法对噪声,对信道阶数误估计,对信道零点位置敏感的缺点。文中仿真证明了在较低信噪比条件下,利用新算法仍可对多输入多输出信道进行较好的估计。     

MIMO OFDM系统中一种基于MMSE准则的优化型Turbo-BLAST检测算法

本文提出了一种MIMO OFDM系统中使用BLAST空时码的优化型Turbo迭代检测算法。该算法采用基于软输出的MMSE-PIC检测器,并在联合考虑信道估计误差与信道空间相关性的基础上,最优化该MMSE滤波器的权系数,从而改善了由于实际中存在信道估计误差和信道空间相关性所导致的软信息提取精度恶化现象。此外,文中进一步推导获取了该最优化权系数的闭式解和迭代循环的外信息表达式。最后,仿真结果也表明在不同的信道估计误差和信道空间相关性条件下,该算法的BER性能总优于传统型软输出的MMSE-PIC迭代检测算法,且计算复杂度相对增加较小。     

基于线性预编码的新型MIMO-HARQ方案

本文研究了多输入多输出系统中基于线性预编码的混合自动请求重传后合并方案,本方案根据各子数据流多次重传所经历的空间子信道增益与功率分配以及当前的空间子信道增益来确定当前数据流的置换方式和功率分配,补偿各子数据流空间增益差异,以获得更多的空间分集增益.此外,在接收端采用最大后验概率准则对重传数据合并进一步提高性能.仿真结果表明,该方案能够有效提高系统吞吐率,降低误比特率.     

基于输出过采样的IIR信道迫零盲均衡

本文提出一种新的针对单输入单输出IIR信道的盲均衡算法.首先通过对信道输出的过采样建立特殊的多信道模型.对多信道模型的输出应用线性预测,证明了预测误差只包含多信道模型冲激响应在第一个时隙的参数,并给出最佳线性预测器的长度.通过预测误差的协方差矩阵可以求解该冲激响应参数.基于该参数可构造出不同时延的迫零均衡器.仿真结果显示了本文算法的有效性.     

AR models for wideband source location

Wideband source location in array signal processing has received much attention in the literature lately. Methods such as the Coherent Signal Subspace (CSS) method proposed by Wang and Kaveh [12], and the signal subspace method used by Cadzow [3], are typical of the approaches used to tackle the multiple wideband source location problem. Most of these methods are variations of the narrowband high-resolution methods. Grenier [5], on the other hand, has applied the idea of time-dependent Auto-Regressive (AR) modeling [7] for a nonstationary process to the frequency domain AR modeling of the sensor outputs in a linear array and has been able to produce good results for a wideband signal. The AR coefficients in the model are expanded in a set of frequency-dependent basis functions. The choice of the basis functions was deemed immaterial and the method works even when only one snapshot of the array output is available. In this paper, we re-examine this method and present an extension of the frequency-dependent AR modeling approach to a planar array. It is shown that the use of a set of sinc functions for representing the frequency-dependent AR coefficients accurately tracks their evolution in the frequency domain, and gives superior performance compared to that when power or Legendre functions are used. We also propose two methods for smoothing the spatial spectra, from which the source locations are determined. Comparison with the CSS method are also presented.Research supported by NSERC and TRIO.     

Multichannel ARMA modeling by least squares circular latticefiltering

The paper makes an attempt to develop least squares lattice algorithms for the ARMA modeling of a linear, slowly time-varying, multichannel system employing scalar computations only. Using an equivalent scalar, periodic ARMA model and a circular delay operator, the signal set for each channel is defined in terms of circularly delayed input and output vectors corresponding to that channel. The orthogonal projection of each current output vector on the subspace spanned by the corresponding signal set is then computed in a manner that allows independent AR and MA order recursions. The resulting lattice algorithm can be implemented in a parallel architecture employing one processor per channel with the data flowing amongst them in a circular manner. The evaluation of the ARMA parameters from the lattice coefficients follows the usual step-up algorithmic approach but requires, in addition, the circulation of certain variables across the processors since the signal sets become linearly dependent beyond certain stages. The proposed algorithm can also be used to estimate a process from two correlated, multichannel processes adaptively allowing the filter orders for both the processes to be chosen independently of each other. This feature is further exploited for ARMA modeling a given multichannel time series with unknown, white input     

Blind identification of an autoregressive system using a nonlineardynamical approach

The problem of identifying an autoregressive (AR) system with arbitrary driven noise is considered here. Using an abstract dynamical system to represent both chaotic and stochastic processes in a unified framework, a dynamic-based complexity measure called phase space volume (PSV), which has its origins in chaos theory, can be applied to identify an AR model in chaotic as well as stochastic noise environments. It is shown that the PSV of the output signal of an inverse filter applied to identify an AR model is always larger than the PSV of the input signal of the AR model. Therefore, by minimizing the PSV of the inverse filter output, one can estimate the coefficients and the order of the AR system. A major advantage of this minimum-phase space volume (MPSV) identification technique is that it works like a universal estimator that does not require precise statistical information about the AR input signal. Because the theoretical PSV is so difficult to compute, two approximations of PSV are also considered: the e-PSV and nearest neighbor PSV. Both approximations are shown to approach the ideal PSV asymptotically. The identification performance based on these two approximations are evaluated using Monte Carlo simulations. Both approximations are found to generate relatively good results in identifying an AR system in various noise environments, including chaotic, non-Gaussian, and colored noise     

Low-complexity nonlinear least squares carrier offset estimator for OFDM: identifiability, diversity and performance

Orthogonal frequency division multiplexing (OFDM) transforms frequency-selective channels into multiple low-rate flat-fading subchannels. Carrier frequency offset between transmitter and receiver local oscillators must be estimated and compensated at the receiver to maintain orthogonality of these subchannels. In this paper, we derive the nonlinear least squares (NLS) estimator for carrier frequency synchronization that exploits receiver diversity and known OFDM signal subspace structure due to the placement of unmodulated (virtual) subcarriers. The resulting estimator benefits from the high-resolution subspace method without the computational overhead associated with subspace decomposition. Fundamental estimator performance relationships against parameters such as signal-to-noise ratio (SNR), frequency-selective fading, and diversity branch correlation are derived. In particular, we derive the Cramer-Rao bound (CRB) for the mean square error (MSE) of the carrier frequency offset estimator. Numerical studies are presented to verify the results.     

多层融合深度局部PCA子空间稀疏优化特征提取模型

子空间方法是主要利用全局信息的经典模式识别方法,随着深度学习思想的引入,局部自学习结构特征模型得到大家的关注.利用深度学习原理,本文提出一种多层融合的深度局部子空间稀疏优化特征自学习抽取模型解决目标识别问题.首先,对训练样本集通过最小化重构误差得到第一层的主成分（Principal Component Analysis,PCA）特征映射矩阵;然后,通过L₁范数约束对特征映射结果进行稀疏优化,提高算法鲁棒性.接着,在第二层映射层以第一层的特征输出为输入,进行同样的特征矩阵学习操作,最终将图像映射至深层PCA子空间;然后,对各个映射层的特征提取结果进行加权融合,进行二值化哈希编码和直方图分块编码,提取图像的深度子空间稀疏特征.在FERET、AR、Yale等经典人脸数据库以及MNIST、CIFAR-10等目标数据库上的实验结果表明,该算法可以取得较高的识别率以及较好的光照、表情、人脸朝向鲁棒性,并且相对于卷积神经网络等深度学习框架具有结构简洁、收敛速度快等优点.     

Linear bispectrum of signals and identification of nonminimum phaseFIR systems driven by colored input

The identification of non-minimum-phase finite-impulse-response (FIR) systems driven by third-order stationary colored signals that are not linear processes is addressed. Modeling the linear part of the bispectrum of a signal is discussed. The bispectrum of a signal is decomposed into two multiplicative factors. The linear bispectrum is defined as the factor of the bispectrum that can exactly be modeled using a third-order white-noise-driven linear shift-invariant (LSI) system. The linear bispectrum of the output of the unknown LSI system is represented using an ARMA (autoregressive moving average) model, where the MA parameters correspond to the unknown FIR system impulse response coefficients, and the AR parameters model the linear bispectrum of the input signal. An algorithm for identifying the MA and AR parameters is given. How the proposed method is different from fitting an ARMA model directly to the bicumulants or the bispectrum of the system output is discussed. The method is applied to blur identification     

Blind identification of IIR systems based on special SIMO model

Based on oversampling the system output, this paper presents a deterministic approach to blind identification of fast changing infinite-impulse-response (IIR) systems. The contributions of this paper are: 1) we prove that oversampling the output of a single-input-single-output (SISO) IIR system is equal to transforming the SISO IIR system into a single-input-multiple-output (SIMO) IIR model with all subsystems have the same autoregressive (AR) coefficients. Based on this model, a new identification algorithm is proposed, which can give the least-squares approach; 2) we show that in the SIMO model, the number of subsystems can be varied and will affect the identification performance. We also discuss how to choose a proper subsystem number to guarantee the best performance; 3) we deduce the sufficient and necessary conditions for the system to be identifiable associated with the proposed algorithm. Since the proposed approach only needs a small quantity of data samples, it can be used for fast changing IIR systems. Computer simulations give some illustrative examples.     

Analog Precoder and Equalizer Designs and their Geometry for Multichannel Communication

This is a paper on modulation theory that addresses joint analog precoder and equalizer design for multichannel data transmission over the frequency-selective additive Gaussian noise (AGN) channel. The design goal is to maximize mutual information rate, minimize the mean square error, or minimize the bit error rate subject to a transmit power constraint. We assume a continuous channel model with precoder transmissions for m subchannels that lie in an n-dimensional linear subspace of L²(R). m and n are design parameters. We first design the subspace according to the channel characteristics, and then design the precoders as functions in this subspace. After the design of the optimal precoder and equalizer, we explore the geometry of these designs. We show that all of these precoder and equalizer designs are, in fact, decompositions of a virtual twochannel problem into a system of canonical coordinates, wherein variables in the canonical message channel are correlated only pairwise with corresponding variables in the canonical measurement channel. This finding clarifies the geometry of precoder and equalizer designs and illustrates that they decompose the two-channel communication problem into what might be called the Shannon channel