改进的高效MIMO-OFDM系统EM信道估计算法

针对MIMO-OFDM系统中期望最大化(EM)信道估计算法在高信噪比(SNR)下带来的误差地板(EF)现象,且OFDM符号的数据传输效率随发射天线数的增加而明显降低,提出一种改进的高效EM信道估计算法。该算法首先引入一种准确的等效信号模型并推导出一种修正的EM算法,改善了EM算法在高SNR下的性能;在多个OFDM间利用相位正交导频序列来提高数据传输效率,同时进行联合信道估计以提高估计性能。仿真实验验证了所提算法具有更高的信道估计性能和更高的数据传输效率。     

修正Kalman滤波多带UWB信道估计改进方法

针对多带超宽带(UWB)系统中修正Kalman滤波算法复杂度高的缺陷,提出一种低复杂度的修正Kalman滤波改进方法。该方法中UWB信道采用自回归模型(AR)建模,利用导频跟踪时变信道衰减因子,通过Kalman滤波和频域分段最小均方误差(MMSE)算法同时跟踪信道的时域相关性和频域相关性,提高了系统性能,降低了计算复杂度。仿真结果表明,和修正的Kalman滤波方法相比,在估计精度损失很小的情况下,所提方法极大降低了计算复杂度,提高了系统整体的估计性能。     

基于FG方法的MIMO-OFDM信道估计

为提高MIMO-OFDM系统信道估计的性能,并且提高频谱的整体利用效率,文章提出一种利用信道特性构建因子图模型,结合EM算法进行信道估计的算法。将多输入多输出(MIMO)信道估计问题转化为一系列单输入单输出(SISO)信道估计问题,避免了大规模的矩阵运算。同时将EM算法代替因子图(FactorGraph)时域方向上的迭代,提高了系统性能。仿真实验表明,文章所提的算法能有效提高系统的性能。     

Turbo编码MIMO-OFDM系统中的EM迭代信道估计

该文对频率选择性衰落信道条件下的Turbo编码多入多出正交频分复用(MIMO-OFDM)系统的信道估计进行了研究,提出了一种将Turbo迭代译码与EM迭代信道估计相结合的方法。采用该方法的EM迭代信道估计性能得到了明显提高,且适合于非常恶劣的信道环境。另外,通过仿真可知,利用EM迭代信道估计算法的系统性能优于利用简化的最小二乘(LS)信道估计算法所获得的系统性能。     

采用数据辅助的大规模MIMO信道半盲迭代估计

由于大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)信道衰落参数的维度较高,导致最优估计算法计算量大且需要的导频数较多而影响到频谱效率。为降低计算复杂度并减少导频开销,提出了两种基于期望最大化(Expectation Maximization,EM)估计的半盲迭代改进算法。利用少量正交导频序列估计出信道初值,通过用户与基站间信道的大尺度衰落系数把用户分簇,根据这些系数按比例地分配接收噪声,再利用数据的统计特性推导出信道衰落参数的均值和方差。仿真结果表明,当导频数远少于待估计参数的个数时,半盲估计算法的均方误差(Mean Square Error,MSE)优于导频估计的极大似然(Maximum likelihood,ML)算法。     

应用加权高斯模型的非理想稀疏信道估计

在高速通信系统中,由于多径信道通常存在一些小的散射体,使得抽头向量不满足理想的稀疏特性,导致经典的稀疏估计算法存在一定的性能损失。针对上述非理想稀疏特性问题,提出了一种基于酉变换近似消息传递(Unitary Transform Approximate Message Passing,UT-AMP)和加权高斯(Weighting-Gaussian,WG)先验模型的稀疏估计算法。首先,由非理想稀疏信道的构造分析,导出了WG先验模型和参数;其次,利用贝叶斯公式对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统进行因式分解和因子图建模,归纳了在消息传递框架内期望最大化(Expectation Maximization,EM)算法嵌入方式,推导了联合UT-AMP和EM的信道估计算法;最后,建立仿真环境对所提算法进行复杂度分析和数值仿真。仿真结果表明,所提算法能够以同阶复杂度实现信道估计性能和频带利用率的提升,具有很高的应用和推广价值。     

基于岭回归正则极限学习机的OFDM系统信道估计

针对正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统中信道估计准确度低和算法复杂度高的问题,提出一种基于岭回归正则极限学习机(Regularization Extreme Learning Machine,RELM)的信道估计方法。利用改进的极限学习机网络对信道进行自回归建模,将极限学习机损失函数正则化,通过岭回归方法,可以得到最优的神经网络参数,同时利用先验信道信息完成信道估计。与传统信道估计算法相比,所提信道估计方法仅需较低计算复杂度,即可达到得更低的MSE,从而提供更好的系统误码率性能。     

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。     

广义空间调制系统的低复杂度检测算法

针对广义空间调制( GSM)系统接收端最大似然( ML)检测算法计算复杂度极高的缺点,提出了一种基于压缩感知( CS)信号重构理论的低复杂度信号检测算法。首先,在多输入多输出( MI-MO)信道模型下,通过改进正交匹配追踪( OMP)算法,得到一个激活天线索引备选集;然后,利用ML算法在该备选集中进行遍历搜索,检测出激活天线索引和星座调制符号。仿真结果表明所提算法的检测性能接近于ML算法,且复杂度约为ML算法的2%。因此,所提算法在保证检测性能的同时也大大降低了计算复杂度,实现了检测性能与复杂度之间的平衡。     

MMSE信道估计下的软输出MMSE MIMO检测算法

信道编码的MIMO无线通信系统中,MIMO信号检测器需要输出编码比特的对数似然比,以获得更好的误码率性能.现有的软输出MMSE MIMO检测算法基于理想信道估计得到,在实际信道估计下会导致性能损失.本文针对MMSEMIMO信道估计,基于MMSE信道估计的统计特性,推导了考虑信道估计误差影响的新的软输出MMSE MIMO检测算法.仿真结果表明,相对于现有的软输出MMSE MIMO检测算法,所提出的算法可以显著降低由于信道估计误差导致的残留误码平层,而增加的计算复杂度可以忽略不计;同时,所提算法对信道估计误差方差的相对误差不敏感,具有实际应用的价值.     

Blind channel estimation of space-time Frequency-Shift Keying

The decoupled coherent Maximum Likelihood （ML） detection algorithm presented in this letter can sharply reduce the complexity of the receiver as well as provide better error performance under the precondition that channel should be estimated first. Considering the bandwidth inefficiency of Frequency Shift Keying （FSK）, the acquisition of channel state information through training sequences will further decrease the transmission efficiency. This letter presents a blind channel estimation algorithm based on noise subspace theory which can acquire channel information without any training symbols. The simulation shows that the algorithm brings about fewer channel estimation errors while the frequency efficiency can be increased.     

Effects of imperfect channel sensing and estimation on the performance of cognitive radio systems

In realistic scenarios of cognitive radio (CR) systems, imperfect channel sensing may occur due to false alarms and miss detections. Channel estimation between the secondary user transmitter and another secondary user receiver is another challenge in CR systems, especially for frequency‐selective fading channels. In this context, this paper presents a study of the effects of imperfect channel sensing and channel estimation on the performance of CR systems. In particular, different methods of channel estimation are analyzed under channel sensing imperfections. Initially, a CR system model with channel sensing errors is described. Then, the expectation maximization (EM) algorithm is implemented in order to learn the channel fading coefficients. By exploiting the pilot symbols and the detected symbols at the secondary user receiver, we can estimate the channel coefficients. We further compare the proposed EM estimation algorithm with different estimation algorithms such as the least squares (LS) and linear minimum mean square error (LMMSE). The expressions of channel estimates and mean squared errors (MSE) are determined, and their dependencies on channel sensing uncertainty are investigated. Finally, to reduce the complexity of EM algorithm, a sub‐optimal algorithm is also proposed. The obtained results show that the proposed sub‐optimal algorithm provides a comparable bit error rate (BER) performance with that of the optimal one yet with less computational complexity.     

基于奇异值分解的MIMO-OFDM系统角域MAP信道估计算法

最大后验概率信道估计算法应用于多输入多输出-正交频分复用(MIMOOFDM)系统时需要大规模的矩阵求逆和乘积运算,且系统数据传输效率随发送天线数的增加明显降低.为克服这些问题,提出了一种基于奇异值分解的角域最大后验概率信道估计算法.该算法通过期望最大化把(MIMO)信道估计问题简化为一系列独立的单输入单输出(SISO)问题,并使用奇异值分解避免了大规模矩阵求逆和乘积运算;通过多个OFDM符号联合估计信道提高了系统数据传输效率及算法的估计性能.仿真实验验证了此算法的有效性.     

Low-complexity SAGE-based MMSE channel estimation for CoMP multi-user systems

Coordinated multiple point(CoMP) transmission/reception has been investigated recently as a promising technology to increase the cell-edge user performance of long term evolution-advanced(LTE-A),and channel estimation is a crucial technology for CoMP systems.In this paper,we consider a reduced-complexity minimum mean square error(MMSE) channel estimator for CoMP systems.The estimator uses space alternating generalized expectation maximization(EM)(SAGE) algorithm to avoid the inverse operation of the joint MMSE estimator.In the proposed scheme,the base stations(BSs) in the CoMP system estimate the channels of all the coordinated users serially and iteratively.We derive the SAGE-based estimators and analyze complexity.Simulation results verify that the performance of the proposed algorithm is close to the joint MMSE estimation algorithm while reducing the complexity greatly.     

On the Performance of FFT/DWT/DCT-based OFDM Systems with Chaotic Interleaving and Channel Estimation Algorithms

The efficiency of data transmission over fading channels in orthogonal frequency division multiplexing (OFDM) systems depends on the employed interleaving method. In this study, we propose an improved chaotic interleaving scheme which aims to improve the performance of OFDM system under fading channel. In the proposed scheme, the binary data is interleaved with chaotic Baker map prior to the modulation process. In the sequel, significant degree of encryption is being added during data transmission. The performance of the proposed approach is tested on the conventional fast Fourier transform OFDM, discrete wavelet transform OFDM, and discrete cosine transform OFDM with and without chaotic interleaving. Furthermore, an expectation–maximization (EM) algorithm is proposed for improving channel impulse response (CIR) estimation based on a maximum likelihood principle. The proposed scheme makes use of EM algorithm to update the channel estimates until convergence is reached. The simulation results show the efficiency of the proposed algorithms under Rayleigh fading environments where the symbol error rate essentially coincides with that of the perfect channel case after the fifth EM iteration.     

编码辅助信噪比估计算法的性能分析

本文研究了基于编码辅助的信噪比期望最大（EM）估计算法。在分析编码辅助算法模型的基础上,建立了无编码辅助算法与编码辅助算法的联系,将无编码辅助算法看作是编码辅助算法的一种特殊情况。此外,将同步估计中的嵌入估计算法运用到信噪比迭代估计中,得到了一种简化的编码辅助算法,克服了原算法计算量过大的缺点。仿真结果表明,简化后的算法在降低算法复杂度的同时,仍能保持原算法的估计性能。     

基于EM的联合信道参数估计和数据检测算法

为提高OFDM系统的传输效率,考虑正交频分复用(OFDM)系统的特殊结构,该文提出一种使用少量导频的EM(Expectation Maximization)迭代信道参数估计算法。在每次迭代中不但更新信道冲激响应而且更新噪声方差的估计值。为进一步提高算法的收敛速度,提出联合信道参数估计和数据检测算法。仿真结果表明,在插入较少导频时,基于EM的信道估计算法仍能收敛到给定参数的信道,联合信道参数估计和数据检测算法迭代次数明显减少,并且保持系统的误码率性能不变。     

一种TD-SCDMA载波频偏与信道联合估计算法

通过分析时分同步码分多址(TD-SCDMA)系统的载波频偏和信道特性,提出一种频偏与信道联合估计算法。该算法通过对训练序列部分相关,然后做差分将频偏和信道特性解耦,并通过逐次迭代最终精确估计出频偏和信道参数。仿真结果表明,算法频偏估计性能接近Cramr-Rao界,准确度较高;全链路仿真误码率性能优越,信道估计准确。算法复杂度不高,实用性强,同样适用于其它的TDD模式的CDMA系统。     

垂直分层空时码的MAP检测器及其神经网络实现

该文基于最大后验概率准则,给出了垂直分层空时码的最佳检测箅法。算法对接收到的符号矢量的符号分量进行逐个地判决以使误符号率最小。同时还详细说明了径向基函数神经网络可以完全等价于最大后验概率检测算法,由于神经网络的硬件可实现性,本文提出的算法是可能在实际中应用的。     

基于MUSIC和ML方法的MIMO系统参数估计

该文提出了一种基于MUSIC和ML方法联合估计MIMO系统频偏和信道增益的算法,该算法首先使用MUSIC方法估计出多个发射天线到某一接收天线的频偏子集,然后利用最大似然方法在这个有限子集中分离出不同天线对之间的频偏,最后在频率同步的基础上利用最大似然估计器对信道增益进行估计。该算法解决了在估计多个频偏时直接使用最大似然估计进行多维搜索的问题,将多维搜索转化为一维搜索,降低了算法的复杂度。