基于导频的发射分集OFDM系统的子空间跟踪信道估计

由于发射分集技术可以大大提高系统的抗衰落性能,因此得到了广泛的研究和应用。该文提出了3种基于导频的发射分集正交频分复用(OFDM)系统的子空间幅度跟踪信道估计方法,并分析比较了其估计性能。利用信道传播时延慢变和衰落幅度快变的特点,通过对多径信道的时延子空间和衰落幅度的跟踪,可以部分消除信道估计过程中噪声的影响,大大提高信道估计精度。在信道阶数已知或使用相同秩估计方法的情况下,第3种方法的运算复杂度最低, 性能最好;第1种方法次之,性能最差;第2种方法由于需要进行DFT和IDFT,运算复杂度最高。仿真结果表明,3种子空间幅度跟踪信道估计方法在410-3 误码率时可以提高系统误码率性能1～2 dB左右。     

基于时频子空间投影的LOFDM系统时域相关信道估计算法

Strohmer T与Beaver S于2003年提出了适用于时频散射信道的网格正交频分复用 (LOFDM, Lattice Orthogonal Frequency Division Multiplexing)系统,与传统OFDM系统相比该系统具有更高的频带利用率和更好的误码性能。LOFDM系统发送端需要自适应地调整信号的原形脉冲和其时频分布的参数,使之与信道保持匹配;而接收端则需要准确的信道估计以实现相干解调。本文针对LOFDM 系统由于其信号的时频交错特性接收端无法直接利用接收信号进行时域信道估计的问题,采用一种低复杂度的等效时频子空间投影方法有效分离奇偶子载波,并在此基础上提出了一种LOFDM系统频率选择性块衰落信道条件下的时域信道估计算法。同时还对系统存在的干扰进行了分析,推导得到LOFDM系统信道估计算法的Cramér-Rao理论下界。仿真表明:提出的LOFDM信道估计算法不仅具有较好的均方误差性能,而且近似达到理想信道估计的误符号率性能。      

基于有限字符特性的空时OFDM系统决策反馈信道估计

该文提出了一种适用于空时编码正交频分复用(STC-OFDM)系统盲信道估计算法,利用信号的字符有限特性获取粗信道估计,进而利用映射星座先验知识,采用决策反馈方法得到信道精估计。充分利用了信号的先验知识,以较高的计算复杂度为代价,提高了盲信道估计精度,因此能较好地适用于具有慢时变频率选择性较强的信道,且子载波数不大的系统。与基于子空间盲信道估计算法比较验证了该算法性能。     

基于导频信号的OFDM通信系统信道估计与跟踪

讨论了在正交频分复用(OFDM)通信系统中,基于导频信号,采用投影逼近子空间(PAST)算法实现信道的跟踪估计,在快速变化的信道中,信道阶数自动跟踪,与传统的信道估计方法相比,信道估计精度提高,均方误差减小.通过计算机模拟仿真,验证了信道估计的跟踪性能得到了改善.     

上行3D-MIMO中利用结构稀疏低秩特性的信道估计算法

3维多输入多输出(3D-MIMO)系统能有效提升频谱效率,提高系统容量。但用户数和天线数的剧增,无法保证所有用户的导频都正交,给3D-MIMO信道估计带来估计精度下降和复杂度增加等问题。该文分析了上行3D-MIMO系统信道的结构稀疏特性和低秩特性,并基于这些特性提出一种信道估计算法,给出了算法的收敛性和复杂度。仿真结果表明估计算法能准确地恢复3D-MIMO的信道系数,并有较低的复杂度。     

大规模MIMO系统上行链路时间-空间结构信道估计算法

针对大规模多入多出(MIMO)系统上行链路非平稳空间相关信道的估计问题,该文利用信道的时间-空间2维稀疏结构信息,应用狄利克雷过程(DP)和变分贝叶斯推理(VBI),设计了一种低导频开销和计算复杂度的信道估计迭代算法,提高了信道估计精度。由于平稳空间相关信道难以适用于大规模MIMO系统,该文借助于狄利克雷过程构建了非平稳空间相关信道先验模型,可将具有空间关联的多个物理信道映射为具有相同时延结构的概率信道,并应用变分贝叶斯推理设计了低导频开销和计算复杂度的信道估计迭代算法。实验结果验证了所提算法的有效性,且具有对系统关键参数鲁棒性的优点。

     

OFDM系统中的信道自适应估计与跟踪算法比较

传统的QR分解和投影逼近子空间（PAST）分解算法,可用于矩阵的奇异值分解和秩数的估计。讨论了在正交频分复用（OFDM）通信系统中基于这两种分解方法对导引信号的子空间进行分解,实现信道矩阵的自适应跟踪估计。采用这种算法,降低了矩阵运算的维数,使每个符号期间信道估计的运算量减少,信道估计的均方误差减小,接收机的误码性能得到改善,同时利用计算机模拟,对两种算法运用到信道估计中的跟踪性能进行了比较。     

OFDM系统中基于PM的时延估计算法

在正交频分复用(OFDM)系统的超分辨时延估计中,针对多重信号分类(MUSIC)算法的特征分解计算复杂度较高的问题,给出一种基于传播算子(PM)的时延估计算法。对OFDM系统进行信道估计,根据信道估计结果计算协方差矩阵,并利用协方差矩阵计算PM,然后根据PM构造出噪声子空间并将其标准正交化,最后利用伪谱函数进行时延估计。仿真结果和复杂度分析表明,在复杂度大幅度下降的条件下,所提算法与MUSIC性能相当,且逼近克拉美罗界。     

基于BP神经网络的OFDM系统信道估计

针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中传统信道估计算法复杂度高或估计精度低的问题,给出一种基于反向传播(Back Propagation,BP)神经网络的信道估计方法.采用Simulink仿真工具构建OFDM信号采集平台,建立了基于BP神...     

无线OFDM系统传输信道模型化参数检测

利用导频子载波时频相位相关性统计检测与QAM信号解映射判决误差统计，提出了一种适用于频域导频无线OFDM系统的传输信道模型化参数检测方案，包括信道最大多径时延、最大多普勒频率和信噪比检测；能够有效解决传统的OFDM信道估计算法通常按照最恶劣信道情况上限进行设计，以及基于MMSE准则最优信道估计器中信道先验信息统计运算复杂度高的缺陷；仿真结果表明，在低复杂度条件下可以有效获知当前传输信道模型参数的近似统计信息。     

Low complexity channel estimation for space-time coded wideband OFDM systems

In this article, channel estimation for space-time coded orthogonal-frequency division multiplexing (OFDM) systems is considered. By assuming that the channel frequency response is quasi-static over two consecutive OFDM symbols, we develop channel parameter estimators based on the use of space-time block coded (STBC) training blocks. Using an STBC training pattern, a low-rank Wiener filter-based channel estimator with a significant complexity reduction is proposed. A simplified approach for the optimal low-rank estimator is also proposed to further reduce the estimator complexity while retaining an accurate frequency domain channel estimation. Numerical results are provided to demonstrate the performance of the proposed low complexity channel estimators for space-time trellis coded OFDM systems.     

无线OFDM系统中基于导频的信道估值器的比较分析

本文对无线OFDM系统中基于导频的信道估值器进行了比较分析,对比了四种估值器在均方误差（MSE）上的性能。本文针对时频两维的信道估值,分别分析了时频分离和时频不分离两种处理方法。文中还讨论了信道估值的低阶近似算法,并进行了给定复杂度下的不同估值器的性能对比。结果表明,在相同的复杂度下,使用时频分离的估值器比时频不分离的估值器性能要好,分离估值器经过低阶近似,性能得到了进一步的提高。     

基于多中继导频频分复用的协同通信系统信道估计算法

本文针对频率选择性衰落环境下结合正交频分复用技术的放大转发多中继协同通信系统,提出了基于导频频分复用的频域信道估计算法,包括最小二乘（LS）估计算法和线性最小均方误差（LMMSE）估计算法。理论分析和仿真结果表明,该算法不仅成功分辨了多中继协同通信系统的所有频域信道衰落系数,避免了各中继节点转发的导频符号在目的节点上的混叠干扰,而且减少了频域信道估计所需的导频符号数量和时隙周期长度,提高了协同通信系统的传输效率和频谱利用率,同时显著提高了信道估计的精度,降低了算法的复杂度,具有较高的实用价值。      

语音识别中基于低秩约束的本征音子说话人自适应方法

该文提出一种基于低秩约束的本征音子(Eigenphone)说话人自适应方法。原始的本征音子说话人自适应方法在自适应语料充分时具有很好的效果,然而当自适应语料不足时,出现严重的过拟合现象,导致自适应后的系统可能比自适应前的系统还要差。首先,对协方差矩阵为对角阵的隐马尔可夫-高斯混合模型语音识别系统,推导出一种简化的本征音子矩阵估计算法;然后,对本征音子矩阵引入低秩约束,采用矩阵的核范数作为矩阵秩的凸近似,通过调节核范数的权重因子以有效控制自适应模型的复杂度;最后,给出一种加速近点梯度算法以求解新算法中引入的带有核范数正则项的数学优化问题。汉语连续语音识别的说话人自适应实验表明,引入低秩约束后,本征音子说话人自适应方法的自适应效果得到了明显提高,在5~50 s的自适应数据条件下,均取得了比最大似然线性回归后接最大后验(MLLR+MAP)自适应更佳的识别效果。     

Reduced-rank channel estimation for time-slotted mobile communication systems

In time-slotted mobile communication systems with antenna array at the receiver, the space-time channel matrix is conventionally estimated by transmitting pilot symbols within each data packet (or block). This work is focused on reduced rank (RR) estimation methods that exploit the low-rank property of the space-time channel matrix to estimate single or multiple user channels from the observation of single or multiple training blocks. The proposed RR methods allow to improve the estimate accuracy by reducing the set of unknown parameters (rank reduction) and extending the training set (multiblock processing). The maximum likelihood RR estimate is obtained as the projection of the prewhitened full-rank (FR) estimate onto the spatial or temporal signal subspace. The paper shows that, even for time varying channels, these subspaces can be considered to be slowly varying, and therefore, they can be estimated with increased accuracy by properly exploiting training signals from several blocks. The analytical and numerical performance in terms of mean square error for the RR estimate shows that the main advantage of the proposed method with respect to the conventional FR one can be ascribed to the reduced complexity of the channel parameterization.     

Weighted truncated nuclear norm regularization for low-rank quaternion matrix completion

In recent years, quaternion matrix completion (QMC) based on low-rank regularization has been gradually used in image processing. Unlike low-rank matrix completion (LRMC) which handles RGB images by recovering each color channel separately, QMC models retain the connection of three channels and process them as a whole. Most of the existing quaternion-based methods formulate low-rank QMC (LRQMC) as a quaternion nuclear norm (a convex relaxation of the rank) minimization problem. The main limitation of these approaches is that they minimize the singular values simultaneously such that cannot approximate low-rank attributes efficiently. To achieve a more accurate low-rank approximation, we introduce a quaternion truncated nuclear norm (QTNN) for LRQMC and utilize the alternating direction method of multipliers (ADMM) to get the optimization in this paper. Further, we propose weights to the residual error quaternion matrix during the update process for accelerating the convergence of the QTNN method with admissible performance. The weighted method utilizes a concise gradient descent strategy which has a theoretical guarantee in optimization. The effectiveness of our method is illustrated by experiments on real visual data sets.     

A generalization of weighted subspace fitting to full-rank models

The idea of subspace fitting provides a popular framework for different applications of parameter estimation and system identification. Previously, some algorithms have been suggested based on similar ideas, for a sensor array processing problem where the underlying data model is not low rank. We show that two of these algorithms (DSPE and DISPARE) fail to give consistent estimates and introduce a general class of subspace fitting-like algorithms for consistent estimation of parameters from a possibly full-rank data model. The asymptotic performance is analyzed, and an optimally weighted algorithm is derived. The result gives a lower bound on the estimation performance for any estimator based on a low-rank approximation of the linear space spanned by the sample data. We show that in general, for full-rank data models, no subspace-based method can reach the Cramer-Rao lower bound (CRB)     

On channel estimation and detection for multicarrier signals infast and selective Rayleigh fading channels

Time-domain channel estimation and detection techniques are presented for multicarrier signals in a fast and frequency-selective Rayleigh fading channel. As a consequence of the time-varying channel, the orthogonality between subcarriers is destroyed in conventional frequency-domain approaches, resulting in interchannel interference, which increases an irreducible error floor in proportion to the normalized Doppler frequency. An important feature of the proposed technique is the ability to exploit the time-selective channel as a provider of time diversity. This enables us to achieve performance superior to any other structure without increasing bandwidth or incorporating redundancy, in order to reduce the complexity of the estimator, we apply the theory of optimal low rank approximation to a minimum mean squared error channel estimator and present a theoretical calculation of mean squared error and simulations to confirm that the estimator is robust to changes in channel characteristics     

基于平行因子分析的SIMO-OFDM系统盲信道与符号联合估计算法

针对SIMO-OFDM系统下的信道估计和符号检测问题,该文建立了接收数据矩阵的平行因子分析(PARAFAC)模型,利用PARAFAC模型中离散傅里叶变换矩阵的行满秩特性,结合数据矩阵的奇异值分解,提出了一种信道与符号联合盲估计的闭式求解方法。由于提出的求解方法无须进行迭代便可以完成信道估计和符号检测,因此其计算复杂度低,此外,利用PARAFAC模型实现信道和符号的同时计算,避免了因信道估计误差导致的符号误码率性能下降问题。仿真结果表明,与传统方法相比提出的方法计算复杂度更低,估计性能更好。