OFDM中基于子空间分解的半盲信道估计

针对OFDM系统中盲信道估计计算量大、收敛速度慢和估计精度低的缺点,以及传统信道估计方法的频带利用率低的缺陷,详细介绍了基于子空间分解的半盲信道估计方法。该方法利用接收信号的相关矩阵特性来估计信道,其是传输速率和收敛速度的折衷,通过Matlab软件仿真表明,此方法能较大地改善信道估计精度和降低系统的误差。仿真结果对信道估计问题的深入研究具有借鉴意义。     

MIMO—OFDM系统基于子空间的盲信道估计技术

MIMO-OFDM系统能在宽带无线信道中达到很高的速率。文章建立了MIMO-OFDM信道识别的条件并实现了基于子空间方法的盲信道估计技术。该方法将现存的在SISO-OFDM系统中的基于子空间的盲信道估计进行统一，并且推广到MIMO-OFDM系统中。特别是该方法可以获得信道的精确估计，且能快速收敛。仿真实验的结果显示了该方法的均方误差性能。     

基于压缩感知的加权宽带谱重构算法

压缩感知利用宽带无线信号的频域稀疏特性,能够在低于奈奎斯特速率的采样下利用少量观测数据实现宽带频谱估计和空穴检测。但相关频谱压缩感知算法的性能并不理想,为了实现宽带信道的快速准确感知,本文基于宽带信道的时频统计特性,在去噪基追踪算法(BPDN)的基础上提出了一种优化的加权去噪算法(WBPDN)。该算法利用子频段历史平均功率密度水平来构建各子频段权重以优化目标函数,改善算法性能。实验结果表明:该算法能通过少量观测数据准确重构宽带信道的谱估计,且比传统的BPDN和OMP算法具有更好的压缩性能及更小的重构误差;另外加权后的算法收敛速度更快,显著减少了算法所需的运行时间。      

移动水声通信多径传输非一致多普勒估计方法研究

现有水声通信中的多普勒估计方法一般都假定多径信道每条路径的多普勒因子相等,当各路径多普勒因子存在差异时,该类方法往往无法正确估计出每径的多普勒因子,导致补偿后的信号存在较大误差。为此该文在分析水声信道稀疏特性的基础上,提出一种基于稀疏表示理论的非一致多普勒估计方法。该方法结合水声多径信道与非一致多普勒稀疏特性生成包含信道信息的超完备字典,将每径的非一致多普勒因子估计转化为基于超完备字典的稀疏重构求解,实现非一致多普勒因子的准确估计。仿真结果表明,所提方法不仅能估计出差异较大的非一致多普勒因子,而且对于大多普勒频偏的估计也相当有效,特别适合于高速移动水声通信中的多普勒估计。     

一种基于子带分解技术的宽带盲均衡器结构和算法

高速宽带无线通信中,多径传输信道可能导致几百个符号间的相互干扰,这使得接收端的线性盲均衡器的收敛速度极其缓慢。基于子带分解技术,该文提出了一种适合于高速宽带无线传输的盲均衡器结构及算法。该结构将子带分解技术和全频带的子卷积方法有机结合在一起,明显地加快了高速宽带传输条件下线性盲均衡器的收敛速度;同时通过对接收数据进行降低速率的并行处理,该结构还能减小运算复杂度,有利于工程的实时实现。仿真实验结果验证了文中提出的结构和算法的有效性。     

单载波信号波特率参数的宽带估计算法

宽带信道化接收中常常采用均匀子带技术,当宽带接收数据中的信号参数未知时,均匀子带信道化易造成信号频谱割裂。本文针对该问题建立了单载波信号子带信道化输出的数学模型,通过将信号的频谱损失视为码元成型函数的变化,证明了子带信号保留了原信号的循环平稳特性,提出了一种新的基于子带信号复包络平方谱的波特率估计算法。该方法不需要重构出原信号,利用频谱有损的子带信号就能进行参数估计,提高了宽带处理的效率。仿真实验给出了在不同带宽覆盖比和不同信噪比下算法的估计性能。理论分析和仿真结果验证了算法的有效性和可靠性。     

基于稀疏重构的色噪声背景下未知线谱信号估计

针对色噪声背景下的未知线谱信号估计问题,该文提出一种基于分子频带处理的稀疏重构类线谱估计方法。首先,利用多速率余弦调制滤波器组对观测信号进行子带分解,得到功率谱相对平坦的子带信号。之后,在每个子带信号上,利用基于迭代最小化的稀疏学习方法进行线谱估计,并将各子带上的线谱估计结果进行频域综合滤波以及门限判决等处理。最终得到色噪声背景下的线谱估计结果。理论推导及仿真实验表明所提方法在色噪声背景下具有较好的线谱估计性能。其能够有效地去除色噪声背景,同时保留稀疏重构类线谱估计方法所具有的高频率分辨力等优点。     

MB-OFDM系统的DOA估计方法研究

对信号的波达方向（DOA）估计是超宽带（UWB）定位的关键技术之一,传统的DOA估计方法都是针对窄带信号,不适用于宽带信号。多频带正交频分复用（MB-OFDM）系统中,在信道长度不大于循环前缀长度时,各子载波可依然保持正交性,针对这一特性,将子载波信号解调和重构,通过空间谱估计方法估计出信号的来波方向。仿真结果表明,算法简单易行,估计效果良好,能工作在较低的信噪比下,具有很强的鲁棒性。     

快速的ZP-OFDM系统中的盲信道估计算法

基于子空间的盲信道估计因为其节约带宽和发射功率成为OFDM系统中比较热门的研究领域。但是,之前很多子空间算法有的收敛速度快,计算复杂度却高,有的用迭代算法跟踪子空间的特征结构,复杂度较低,但收敛速度却比较慢。提出了一种基于快速收敛的LMS-Newton算法的ZP-OFDM系统的信道估计方法,既提高了收敛速度,计算复杂度又不是很高。借助于子空间跟踪,该算法可以自适应地估计信道相关矩阵的噪声子空间,从而估计OFDM系统信道。仿真结果表明该算法可以改善信道估计的性能。     

分布式压缩感知实现联合信道估计的方法

针对无线通信中多个信道之间存在相关性的现象,本文研究了基于压缩感知的联合信道估计。通过选取多个节点与簇头之间的信道为研究背景,本文建立了多信道下的联合信道估计模型,推导了判决门限与信噪比之间的关系,提出了基于门限自适应-正交匹配追踪联合重构技术(TA-SOMP)的信道估计算法,并进行了相应的仿真实验。仿真结果表明:与经典的正交匹配追踪(OMP)算法相比,本文算法所重构的信道与原始信道之间的均方误差(MSE)更小,传输信号误比特率(BER)更低;在相同信噪比环境下,TA-SOMP算法所需导频数量更少,频带利用率更高。      

基于子带SDL的宽带自适应波束形成

针对宽带波束形成通常需要大量阵元或延迟线所带来的硬件开销较大,波束形成效率相对较低的问题,提出一种基于子带阵元延迟线（SDL）的宽带自适应波束形成算法。该算法首先建立子带SDL模型,然后利用分析滤波器组将阵元接收的宽带信号分解为子带信号并进行相应的子带线性约束最小方差（LCMV）波束形成,最后通过综合滤波器组得到全带的波束形成。仿真结果表明,子带波束形成不仅具有比全带波束形成更高的效率,更好的频率不变性,更强的抗干扰能力及更快的收敛速度,而且可以大大降低硬件开销。      

基于韦伯分布函数的低复杂度变步长符号算法

针对OFDM系统中传统信道估计算法在冲击噪声环境中性能急剧下降的问题,提出了一种基于韦伯分布函数的顽健型变步长符号算法进行信道估计。在深入研究冲击噪声特性及韦伯分布函数性质的基础上,提出了采用估计误差绝对值的韦伯分布函数控制步长的低复杂度变步长符号算法。该算法在利用传统符号算法顽健性的基础上,采用估计误差的韦伯分布函数动态地改变迭代符号算法的步长,从而能够以较低的复杂度提高变步长符号算法在冲击噪声环境中的收敛速度。算法复杂度分析及仿真结果表明,在冲击噪声环境下所提算法相较于传统自适应滤波信道估计算法能够以更低的复杂度、更快的收敛速度达到相同的信道估计均方误差。     

Alias-Free Subband Adaptive Filtering With Critical Sampling

To overcome the limitations of a conventional fullband adaptive filtering, various subband adaptive filtering (SAF) structures have been proposed. Properly designed, an SAF will converge faster at a lower computational cost than a fullband structure. However, its design should consider the following two facts: the interband aliasing introduced by the downsampling process degrades its performance, and the filter bank in the SAF introduces additional computational overhead and system delay. In this paper, to fully exploit the benefits of using an SAF, an almost alias-free SAF structure with critical sampling is proposed. The interband alising is removed from the subband signal by isolating the aliasing using a bandwidth-increased analysis filter. Computer simulations show that the proposed structure converges faster than both an equivalent fullband structure at lower computational complexity and recently proposed SAF structures for a colored input.     

Performance analysis for subband identification

The so-called subband identification method has been introduced recently as an alternative method for identification of finite-impulse response systems with large tap sizes. It is known that this method can be more numerically efficient than the classical system identification method. However, no results are available to quantify its advantages. This paper offers a rigorous study of the performance of the subband method. More precisely, we aim to compare the performance of the subband identification method with the classical (fullband) identification method. The comparison is done in terms of the asymptotic residual error, asymptotic convergence rate, and computational cost when the identification is carried out using the prediction error method, and the optimization is done using the least-squares method. It is shown that by properly choosing the filterbanks, the number of parameters in each subband, the number of subbands, and the downsampling factor, the two identification methods can have compatible asymptotic residual errors and convergence rate. However, for applications where a high order model is required, the subband method is more numerically efficient. We study two types of subband identification schemes: one using critical sampling and another one using oversampling. The former is simpler to use and easier to understand, whereas the latter involves more design problems but offers further computational savings.     

Robust microphone arrays using subband adaptive filters

A new adaptive beamformer which combines the idea of subband processing and the generalised sidelobe canceller structure is presented. The proposed subband beamformer has a blocking matrix that uses coefficient-constrained subband adaptive filters to limit target cancellation within an allowable range of direction of arrival. Simulations comparing the fullband and subband adaptive beamformers show that the subband beamformer has better performance than the fullband beamformer when the target and/or interfering signals are coloured. In reverberant environments, the proposed subband beamformer also performs better than its fullband counterpart     

基于多相滤波器组的非线性回声抵消

在免提通话系统和移动通信设备中，扬声器常常工作在较高的音量下，容易发生过载现象，从而产生明显的非线性声学回声，这在小微型扬声器中更加常见。常用的线性AEC(Acoustic Echo Cancellation)算法无法消除此类非线性回声，因此通话质量受到严重影响。非线性回声主要表现为额外的高频谐波分量，这些分量使得全带系统不再满足线性关系，而通常的AEC算法都是基于最小化全带误差推导而来，因此性能很容易受到非线性失真的影响。本文提出了一种基于多相滤波器组的子带AEC算法，把全带误差变成了各个子带的误差，因而把谐波失真成分变成了某些子带内的加性噪声，这使得谐波失真较小的那些子带依然能够正常收敛。通过仿真和实测实验，当出现非线性失真时，新方法的ERLE(Echo Return Loss Enhancement)明显高于经典的全带时域和频域方法，对于非线性失真明显的语音信号，ERLE提升约10 dB。      

Analysis of linear prediction, coding, and spectral estimation fromsubbands

Linear prediction schemes make a prediction xˆ_i of a data sample x_i using p previous samples. It has been shown by Woods and O'Neil (1986) as well as Pearlman (1991) that as the order of prediction p→∞, there is no gain to be obtained by coding subband samples. This paper deals with the less well understood theory of finite-order prediction and optimal coding from subbands which are generated by ideal (brickwall) filtering of a stationary Gaussian source. We first prove that pth-order prediction from subbands is superior to pth-order prediction in the fullband, when p is finite. This fact adduces that optimal vector p-tuple coding in the subbands is shown to offer quantifiable gains over optimal fullband p-tuple coding, again when p is finite. The properties of subband spectra are analyzed using the spectral flatness measure. These results are used to prove that subband DPCM provides a coding gain over fullband DPCM, for finite orders of prediction. In addition, the proofs provide means of quantifying the subband advantages in linear prediction, optimal coding, and DPCM coding in the form of gain formulas. Subband decomposition of a source is shown to result in a whitening of the composite subband spectrum. This implies that, for any stationary source, a pth-order prediction error filter (PEF) can be found that is better than the pth-order PEF obtained by solving the Yule-Walker equations resulting from the fullband data. We demonstrate the existence of such a “super-optimal” PEF and provide algorithmic approaches to obtaining this PEF. The equivalence of linear prediction and AR spectral estimation is then exploited to show theoretically, and with simulations, that AR spectral estimation from subbands offers a gain over fullband AR spectral estimation     

Covariance matrix based fast smoothed sparse DOA estimation with partly calibrated array

To solve the problem of direction-of-arrival (DOA) estimation for partly calibrated array, a new gain-phase error matrix estimation scheme and a smoothed sparse signal reconstruction method tailored for the complex-valued covariance matrix are proposed. In the proposed method, DOA estimation is achieved by employing the structure of the covariance matrix for the error matrix estimation and the complex-valued gradient matrix based fast non-convexity data reconstruction. The proposed method has much faster computational speed than other sparse DOA estimation methods with partly calibrated array. In addition, simulation results show that it performs well and is independent of the errors.     

A new delayless subband adaptive filter structure

Adaptive subband techniques have been developed to reduce complexity and slow convergence problems of the traditional fullband high-order adaptive filters. Some of the disadvantages often encountered in most of the proposed architectures are the effect of aliasing associated with the multirate structure, which is a source of error in the modeling of the unknown system, and the delay introduced in the signal path. We present a new delayless maximally decimated structure where the optimal subband filters are related to the wideband system in a closed form. They make use of a special DFT analysis filterbank where the polyphase components of the prototype filter represent fractional delays so that there is no need for adaptive cross-filters, and the unknown system can modeled perfectly in a closed-loop scheme. We interpret the proposed structure as a special case of a block adaptive filter with lower computational complexity than the conventional fullband LMS algorithm. Some computer simulations are presented in order to verify the good features of the proposed structure