基于Kalman滤波跟踪的MIMO-OFDM半盲信道估计

为克服常用的最小均方(LS)估计算法误差平底较高,且需要占用大量导频资源跟踪时变信道的缺点,提出一种基于Kalman滤波跟踪的MIMO-OFDM半盲的信道估计算法,该算法在采用少量导频的前提下,建立参数化信道模型,采用自适应Kalman滤波技术跟踪时变信道状态信息。仿真表明:该算法能很好地跟踪时变信道状态,显著降低多普勒频移条件下估计的均方误差。     

基于整体最小二乘的联合信道估计及OFDM信号检测算法

针对现有的联合信道估计及OFDM信号检测算法性能不高的问题,该文提出一种基于整体最小二乘的联合信道估计及OFDM信号检测算法。该算法首先通过导频估计初始的信道信息,在此基础上不断地采用整体最小二乘进行OFDM信号检测及信道估计,有效缓解迭代模型误差的影响,加快了算法迭代的收敛速度,提高了信道估计的精度,从而降低了OFDM系统的误码率。该文推导的信道估计克拉美罗界及仿真结果均表明所提出的算法在时变信道环境下优于现有的联合信道估计及OFDM信号检测算法。     

伪叠加Zadoff-Chu序列的LTE下行信道估计

分析了正交频分复用系统中基于叠加训练序列的最小二乘和线性最小均方误差信道估计算法,在此基础上针对LTE信道的结构特征,提出了一种利用伪叠加Zadoff-Chu序列进行信道估计的最小二乘算法。该方法可以大大降低算法复杂度,并且不需要信道先验信息、不占用带宽资源。仿真结果验证了该方法的有效性,其性能比频域的离散傅里叶变换算法和线性最小均方误差算法性能更优。     

基于导频的OFDM信道估计

从最小二乘和最小均方误差两种估计准则出发，结合OFDM多载波并行传输中符号时间长度大于信道冲散响应持续时间的特点，在块状和梳状导频图案下。导出了时域和频域的信道估计算法。最后采用广义平稳非相关散射假设下的多径时变瑞利衰落信道模型。对各个算法进行了仿真,比较了不同导频图案下备算法对系统误符号率的影响，分析结果表明。当导频数大于以采样间隔为量度的等效信道长度时。时域估计比频域估计精度更高;当导频数目小于等效长度时,最小均方估计优于最小二乘估计。     

分布式空频编码协同通信系统中基于导频辅助的频域信道估计算法

针对频率选择性衰落信道下的分布式空频编码协同通信系统,提出了基于导频辅助的各种频域信道估计算法,包括最小二乘、线性最小均方误差、低阶近似线性最小均方误差算法,并给出了相应的均方误差性能表达式.理论分析和仿真结果表明本文提出的信道估计算法在协同通信系统中具有很高的实用性和精确度.     

基于导频的光OFDM系统信道估计

信道估计是光正交频分复用（OOFDM）系统关键技术之一,为了克服光纤色散和模式的传输时延所产生的载波间干扰（ICI）,设计了一种基于导频的信道估计器,通过分析最小二乘（LS）和线性最小均方误差（LMMSE）算法估计原理,仿真实验比较两种算法的误比特率和均方误差性能,实验结果表明,LS与LMMSE在OOFDM系统中都有较好的作用,但LMMSE算法性能要优于LS算法。     

基于导频的OFDM系统信道估计方法研究

正交频分复用(OFDM)技术采用多载波传输方式,成为了第四代移动通信核心技术。OFDM系统可采用基于导频的信道估计方法,提高信道传输的准确性。文章在matlab环境下对块状导频进行仿真,对最小乘二算法和最小均方误差估计算法进行了比较,经过实验得出,块状导频更适合于慢衰落信道。     

快速时变信道下的OFDM信道估计方法

为解决正交频分复用(OFDM)系统在快速时变信道下引入的子载波间干扰(ICI)导致性能急剧下降的问题,提出一种基于带状矩阵的数据辅助型信道估计算法。通过分析由多普勒频偏引入的子载波间干扰矩阵,运用线性模型对信道冲击响应算法进行建模,对每个子载波设计带状补偿矩阵以最大程度消除ICI。数值仿真结果表明,该信道估计算法较最小均方误差(MMSE)算法复杂度大大减小,便于实现,满足快速时变信道下的估计精度需要。     

双边阈值补偿隐藏导频UWB系统信道估计新方法

在隐藏导频信道估计系统中,导频和数据叠加同时发送,造成接收端分离的导频信息中掺杂着数据成分,从而严重干扰信道估计,目前尚无针对这方面的解决方案,因此提出双边阈值补偿算法,该算法应用于发送端。发送数据经过双边阈值补偿算法处理后迫使数据均值为零,然后叠加导频信息并发送出去,接收端用传统的导频数据分离方法就可以得到高质量的导频信息,从而提高信道估计精度。仿真结果表明:和传统隐藏导频信道估计系统相比,在最小二乘和最小均方误差算法下信道估计精度和误码率方面均有明显改善。     

一种基于基扩展模型的OFDM频域快时变信道估计方法

现有的OFDM系统快时变信道估计方法均为时域估计方法,假定信道为整数倍采样信道,利用理想的等间隔均匀导频恢复信道时域响应函数,进而采用基扩展模型拟合并估计其时变特性。而实际的信道通常为非整数倍采样信道,频带内可用的导频也并非假定的理想模式。此时,恢复的信道时域响应出现能量泄漏,导致算法性能大大受限。本文提出一种频域快时变信道估计方法,利用信道频域时变传输函数辅助估计,从而估计得到信道频域响应矩阵。该方法在频域实现,性能不受信道时域响应能量泄露影响。仿真实验从误码率、均方误差两方面分别验证了该方法的有效性。     

Estimation of time-varying channels for pilot-assisted OFDM systems

Orthogonal frequency division multiplexing (OFDM) systems encounter performance degradations because of the time-varying (TV) channels common in wireless environments. The channel variations within one OFDM symbol introduce intercarrier interference. In this case, the frequency domain channel matrix is no longer diagonal, hence the corresponding channel estimation is challenging. In this article, two novel TV channel estimation approaches are proposed for the pilot-assisted OFDM systems, where the channel is approximated by the high-order linear model or the piece-wise linear model in time domain. The least square estimation is derived for the two kinds of channel approximations. The simulation is performed based on realistic TV channels with a fairly high Doppler spread. The results show the significant decreasing of the estimation mean square error using the proposed approaches.     

基于EM算法的改进OFDM时变信道估计

针对正交频分复用（OFDM）系统中的信道时变,基于时变信道的分段线性近似模型,提出一种改进的OFDM时变信道估计方案。该方案通过采用期望最大化（EM）迭代算法来提高符号平均信道脉冲响应的估计精度,从而提高时变信道估计的性能;此外,在迭代过程中进行带状子载波间干扰抑制,不仅进一步提高了时变信道估计的性能,而且降低了实现复杂度。理论分析和仿真结果表明,该算法以较低的复杂度代价有效提高了时变信道OFDM系统的性能。     

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神经网络的OFDM系统信道估计模型,并以均方误差和误码率为主要评价指标,分析了不同网络参数和导频数量对信道估计性能的影响.仿真结果表明,与传统信道估计算法相比,基于BP神经网络的信道估计算法可以提供更优的系统性能,而且可以减少导频数量,提高频带利用率.     

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。      

Robust and Improved Channel Estimation Algorithm for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.     

Joint carrier frequency synchronization and channel estimation for OFDM systems via the EM algorithm

A joint carrier frequency synchronization and channel estimation scheme is proposed for orthogonal frequency-division multiplexing (OFDM) system. In the proposed scheme, carrier frequency synchronization and channel estimation are performed iteratively via the expectation-maximization (EM) algorithm using an OFDM preamble symbol. Moreover, we analytically investigate the effect of frequency offset error on the mean square error (MSE) performance of channel estimator. Simulation results present that the proposed scheme achieves almost ideal performance for both channel and frequency offset estimation.     

Least Square Channel Estimation for Two‐Way Relay MIMO OFDM Systems

This letter considers the channel estimation for two‐way relay MIMO OFDM systems. A least square (LS) channel estimation algorithm under block‐based training is proposed. The mean square error (MSE) of the LS channel estimate is computed, and the optimal training sequences with respect to this MSE are derived. Some numerical examples are presented to evaluate the performance of the proposed channel estimation method.     

A new optimized least-square sparse channel estimation scheme for underwater acoustic communication

In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)-CE algorithm along with polynomial interpolated black widow optimization (PI-BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA-OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL-TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference-free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal-to-noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.     

基于导频和修正Kalman滤波的MIMO-OFDM信道估计方法

提出了一种适用于时间频率选择性衰落信道的MIMO-OFDM系统的组合信道估计方法。采用AR过程对信道进行建模,利用基于导频的低维Kalman滤波算法进行信道估计,并采用LS算法估计时变的信道衰减因子。Kalman滤波跟踪了信道的时域相关性,为了同时跟踪信道的频域相关性,采用了一种基于MMSE(minimum mean square error)的合并器对Kalman滤波算法进行修正。仿真表明,提出的这种组合算法降低了传统的Kalman滤波结构的复杂度,能够跟踪信道的时频变化,改进了基于LS准则的信道估计算法,并且与复杂的高维Kalman滤波算法的信道估计性能相当。