Low Complexity Minimum Mean Square Error Channel Estimation for Adaptive Coding and Modulation Systems

Performance of the Adaptive Coding and Modulation(ACM) strongly depends on the retrieved Channel State Information(CSI),which can be obtained using the channel estimation techniques relying on pilot symbol transmission.Earlier analysis of methods of pilot-aided channel estimation for ACM systems were relatively little.In this paper,we investigate the performance of CSI prediction using the Minimum Mean Square Error(MMSE)channel estimator for an ACM system.To solve the two problems of MMSE:high computational operations and oversimplified assumption,we then propose the Low-Complexity schemes(LC-MMSE and Recursion LC-MMSE(R-LC-MMSE)).Computational complexity and Mean Square Error(MSE) are presented to evaluate the efficiency of the proposed algorithm.Both analysis and numerical results show that LC-MMSE performs close to the wellknown MMSE estimator with much lower complexity and R-LC-MMSE improves the application of MMSE estimation to specific circumstances.

Recursive weighted least squares estimation algorithm based on minimum model error principle

Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.

Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of promi-nently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing(MIMO–OFDM).One among an effective solution for channel estimation in wireless communication system,spe-cifically in different environments is Deep Learning(DL)method.This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder(CNNAE)classifier for MIMO-OFDM systems.A CNNAE classi-fier is one among Deep Learning(DL)algorithm,in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another.Improved performances are achieved by using CNNAE based channel estimation,in which extension is done for channel selection as well as achieve enhanced performances numerically,when compared with conventional estimators in quite a lot of scenar-ios.Considering reduction in number of parameters involved and re-usability of weights,CNNAE based channel estimation is quite suitable and properlyfits to the video signal.CNNAE classifier weights updation are done with minimized Sig-nal to Noise Ratio(SNR),Bit Error Rate(BER)and Mean Square Error(MSE).

ON THE EQUIVALENCE OF PDA ALGORITHM AND SIC-MMSE ALGORITHM

In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrinsic messages,it is concluded that the Probabilistic Data Association(PDA) algorithm is equivalent to the Soft Interference Cancellation plus Minimum Mean Square Error algo-rithm(SIC-MMSE) .

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

基于SDW-MMSE的广义特征值稳健波束形成方法

最大输出信噪比(Signal-to-noise ratio,SNR)准则下,广义特征值(Generalized eigenvalue,GEV)波束形成存在复系数难以控制的问题,在复杂的声学环境中容易导致输出信号严重失真。针对复系数估计问题,本文提出一种基于最小均方误差(Minimum mean square error,MMSE)的复系数估计方法,并通过引入语音失真权重因子(Speech distortion weight,SDW),调节降噪效果和语音失真之间的权重关系,进而提出了基于SDW-MMSE的广义特征值稳健波束形成方法。通过最大似然法估计目标信号和噪音信号的功率谱,进而求解主广义特征向量。进一步基于SDW-MMSE估计复系数,将复系数与主广义特征向量相结合,从而得到基于SDW-MMSE的广义特征值稳健波束形成滤波向量。仿真实验结果表明,本文提出的波束形成方法可有效消除相干噪声和非相干噪声,具有输出信噪比高、语音失真少等稳健性能。

基于ASIT-UKF算法的锂电池荷电状态估计

针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。

基于幅度比值和子空间的频率估计算法

为降低单频正弦复信号的频率估计误差,针对传统Rife算法在较低信噪比条件下幅值最大和次大谱线判断错误问题,利用RootMUSIC算法结果代替快速傅里叶变换(FFT)结果,确定幅值最大和次大谱线序号;同时针对Rife算法在部分频率区间估计误差较其他区间偏差大的问题,采用频移重估思路,进一步降低了整个频率估计区间的估计误差。仿真表明,在较低信噪比条件下,所提RM-Rife算法较Rife算法、Root MUSIC算法和M-Rife算法,具有更优的估计性能,可用于相关工程实践中。

Mobile channel estimation for MU-MIMO systems using KL expansion based extrapolation

In multi-user multiple input multiple output （MU-MIMO） systems, the outdated channel state information at the transmit- ter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity. A simple yet effec- tive solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve （KL） expansion of time- varying channels. In this scheme, channel estimation is done at the base station （BS） rather than at the user terminal （UT）, which thereby dispenses the channel parameters feedback from the UT to the BS. Moreover, the inherent channel correlation and the parsimonious parameterization properties of the KL expan- sion are respectively exploited to reduce the channel mismatch error and the computational complexity. Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error （MSE） and ergodic capacity.

Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

An MMSE Decoding Algorithm without Matrix Inversion in QSTBC

The matrix inversion operation is needed in the MMSE decoding algorithm of orthogonal space-time block coding （OSTBC） proposed by Papadias and Foschini. In this paper, an minimum mean square error （MMSE） decoding algorithm without matrix inversion is proposed, by which the computational complexity can be reduced directly but the decoding performance is not affected.

Statistical-mechanical analysis of multiuser channel capacity with imperfect channel state information

In this paper, the effect of imperfect channel state information at the receiver, which is caused by noise and other interference, on the multi-access channel capacity is analysed through a statistical-mechanical approach. Replica analyses focus on analytically studying how the minimum mean square error （MMSE） channel estimation error appears in a multiuser channel capacity formula. And the relevant mathematical expressions are derived. At the same time, numerical simulation results are demonstrated to validate the Replica analyses. The simulation results show how the system parameters, such as channel estimation error, system load and signal-to-noise ratio, affect the channel capacity.

Image enhancement via MMSE estimation of Gaussian scale mixture with Maxwell density in AWGN

In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data.In wavelet domain,if Bayesian estimator is used for denoising problem,the solution requires a prior knowledge about the distribution of wavelet coeffcients.Indeed,wavelet coeffcients might be better modeled by super Gaussian density.The super Gaussian density can be generated by Gaussian scale mixture(GSM).So,we present new minimum mean square error(MMSE)estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise(AWGN).We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.

CHANNEL ESTIMATION TECHNIQUE IN MULTI-ANTENNA AF RELAY COMMUNICATION SYSTEMS

The channel estimation technique is investigated in OFDM communication systems with multi-antenna Amplify-and-Forward(AF) relay.The Space-Time Block Code(STBC) is applied at the transmitter of the relay to obtain diversity gain.According to the transmission characteristics of OFDM symbols on multiple antennas,a pilot-aided Linear Minimum Mean-Square-Error(LMMSE) channel estimation algorithm with low complexity is designed.Simulation results show that,the proposed LMMSE estimator outperforms least-square estimator and approaches the optimal estimator without error in the performance of Symbol Error Ratio(SER) under several modulation modes,and has a good estimation effect in the realistic relay communication scenario.

基于改进变步长最小均方算法的心电图信号去噪

针对定步长和现有变步长最小均方(LMS)自适应滤波算法对心电图(ECG)信号去噪效果较差的问题,提出一种基于分式函数改进的变步长LMS算法。首先利用分式函数构造出改进的变步长LMS算法的步长函数式,通过理论和仿真分析得到该算法参数的最佳取值,并在相同条件下与定步长和其他变步长LMS算法进行性能对比,验证了该算法具有更快的收敛速度、更低的稳态误差和更小的计算复杂度。然后将该算法与定步长和其他变步长LMS算法在相同条件下对含多种真实噪声的ECG信号进行去噪效果对比。实验结果表明,相比于其他算法,该算法能更好地去除ECG信号的噪声,且去噪后ECG信号的信噪比最大,均方误差最小,Pearson相关系数最接近于1。

基于二维回波数据的距离-多普勒副瓣快速迭代抑制方法

针对距离-多普勒二维高副瓣严重影响强目标附近弱目标的高精度提取,现有方法对非网格目标提取效果不佳等问题,提出了一种基于二维回波数据的距离-多普勒副瓣快速迭代抑制算法。在迭代最小均方误差框架下,该方法基于网格化后的二维回波数据自适应地为每个距离-多普勒单元估计最佳权矢量,同时将同一距离门不同多普勒单元协方差矩阵的求逆转化为同一距离单元协方差矩阵的求逆,在不牺牲精度和检测性能的条件下大大降低了计算复杂度。仿真和实测数据验证了文中方法的有效性。

基于机载多通道雷达迭代超分辨估计的前视成像

波达角估计算法用于机载多通道雷达前视成像时可以突破瑞利极限,实现同一波束主瓣宽度内的多目标分辨,改善成像的方位向分辨率,然而天线波束覆盖有限且其快速扫描使得可用于协方差矩阵估计的数据样本缺乏,导致对目标位置和幅度估计出现误差。该文提出了一种基于单快拍迭代超分辨处理的多通道雷达前视成像算法,通过对单个空域快拍的迭代谱估计可获得目标的准确位置和幅度信息,再通过多个脉冲的非相干累积得到前视方位高分辨成像。仿真和实测数据处理结果表明,所提算法具有分辨多目标的能力,相较于传统前视成像算法显著提高了前视图像的方位分辨率,同时保证了点目标的精确重构和面目标的轮廓重构。

稳健的稀疏信号单快拍波达方向估计

通过稀疏重构得到传感器阵列输出数据的稀疏表示模型,研究了单快拍采样情形下的信号到达角(Direction of Arrival,DOA)估计问题。提出了一种基于最小均方误差(Minimum Mean-Square Error,MMSE)准则迭代实现的单快拍到达角估计算法(Iterative Implementation of MMSE,II-MMSE)。该算法将原有的稀疏表示模型中稀疏信号矢量的求解问题,转化为迭代求解稀疏功率对角阵,进而估计多目标信号的DOA。给出了算法的完整实现流程,从理论上分析了II-MMSE算法的迭代收敛性和对阵列模型误差的鲁棒性。仿真结果表明,II-MMSE算法在低信噪比、相干背景、小样本、阵列未校准等条件下都具有良好的测向精度和多目标分辨能力。

融合蚁群算法MMSE准则在MMW混合波束形成中的应用

波束形成预编码技术是毫米波通信的核心技术,其中模拟和数字混合波束形成技术可以很好地平衡模拟预编码技术和数字预编码技术的优缺点,既可以得到足够的波束形成增益、减少传播损耗,又降低了硬件成本和功耗。均方误差(MSE)是表征毫米波通信传输可靠性的性能指标,利用最小均方误差(MMSE)准则的混合预编码设计可以在毫米波大规模多输入多输出(MIMO)系统中获得较好的频谱效率。将MMSE与广泛应用于解决旅行商问题的蚁群算法相融合,以进一步优化基于流形优化的混合预编码算法。仿真结果表明,融合后的优化算法具有更低的误码率和更好的频谱效率。