Performance of cumulant-based rank reduction estimator in presence of unexpected modeling errors

Compared with the rank reduction estimator(RARE) based on second-order statistics(called SOS-RARE), the RARE based on fourth-order cumulants(referred to as FOC-RARE) can handle more sources and restrain the negative impacts of the Gaussian colored noise. However, the unexpected modeling errors appearing in practice are known to significantly degrade the performance of the RARE. Therefore, the direction-of-arrival(DOA) estimation performance of the FOC-RARE is quantitatively derived. The explicit expression for direction-finding(DF) error is derived via the first-order perturbation analysis, and then the theoretical formula for the mean square error(MSE) is given. Simulation results demonstrate the validation of the theoretical analysis and reveal that the FOC-RARE is more robust to the unexpected modeling errors than the SOS-RARE.

基于MMSE准则的MIMO多中继系统线性收发机设计

本文以放大转发MIMO多中继系统为研究对象,研究其联合线性收发机的优化设计问题,其基本思想是在中继的总发射功率约束下,最小化系统的均方误差(MSE).为降低系统复杂度,本文首先引入了乘积矩阵的奇异值分解,把收发机的设计简化为以奇异值向量和酉矩阵为优化变量的优化问题;接着利用变量替换并引入罚项,将简化后的问题转化成只有酉矩阵约束的优化问题.在此基础上,通过引入替换变量的欧氏梯度,设计了新的黎曼欧氏最陡下降算法,从而有效地处理酉矩阵约束.仿真结果表明,与传统的设计方法相比,本文提出的方案性能最优,最接近MSE下界.

基于LS算法的OFDM信道估计的研究与改进

OFDM信道估计中,LS算法因其运算简单,得到了广泛应用。但是在实际的系统中,由于有非理想因素的存在,使得该算法受到载波间干扰(ICI)噪声的影响;并且突发传输时,信道响应会受到外界噪声的干扰,使估计的信道响应幅值在一定范围内上下波动,并带有尖峰和毛刺。为了解决此问题,提出了一种基于LS算法的最优化FIR滤波器(切比雪夫滤波器)信道估计算法。切比雪夫滤波器在过渡带衰减很快,和理想滤波器的频率响应曲线之间的误差最小,因此该算法较之传统的加窗算法,能保证局部频率点的性能也是最优的,进而有效地减小均方误差(MSE)。在高斯白信道环境下引入突发噪声,对所提方案进行了仿真,其结果验证了该方法能有效消除通带内因突发传输引起的毛峰和尖刺。

加权系数平均法改进的小波域LMMSE信道估计算法

针对传统LMMSE算法需要知道信道特性的问题,提出了一种加权系数平均法改进的小波域LMMSE信道估计算法。运用离散小波变换对LS初始估计和预滤波处理后的信号实行阈值量化去噪处理,然后结合时域信道能量分布的稀疏性特征,利用加权系数平均法求出各子载波的频域响应,从而克服了传统LMMSE算法需要预先知晓信道统计特性的缺陷。对算法的BER和MSE性能进行实验仿真,结果表明:文中所提改进算法的信道估计整体性能显然会更优于LS、SVDLMMSE以及加权平均改进后的LMMSE算法。另外,在信噪比较低且信道统计特性未可知的状况下,文中算法要优于传统的LMMSE算法,并能够较好地降低噪声的影响,有效提升信道估计的精确度。

一种基于特征值的3D MMSE角度域波束赋形算法

将传统二维角度域波束赋形算法应用于三维多输入多输出(3D MIMO)场景时,会导致波形畸变、不稳定甚至失效。为此,在传统二维最小均方误差算法的基础上,提出一种改进的3D MMSE角度域波束赋形算法。在面阵信号模型中利用特征分解法对信号相关矩阵进行分解,去除与噪声相关的小特征值扰动因子,以此解决波形畸变与算法失效问题。仿真结果表明,该算法可实现3D角度域波束赋形,相对3D MVDR算法和ZF算法,其阵列输出均方误差较小,信干噪比较高。

基于新型训练序列的OFDM频率同步算法

针对现有的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)频偏估计算法普遍存在估计范围小,估计精度不高的问题,提出了一种新的训练序列频率同步算法.首先采用缩短训练序列和周期性发送序列的方式,增大了频偏估计的范围,但频偏范围的增大会导致性能的损失;然后又提出一种通过对短周期重复样式的估计值取平均的方法,在保持估计范围不变的情况下,进一步的提高了频偏估计的性能.最后仿真结果表明,改进的算法频偏估计范围大,并且估计精度较高,均方误差(Mean Square Error,MSE)可以达到10^-6.

宽带MIMO-OFDM系统的信道估计方法

为节省系统带宽,提出了宽带M IMO-OFDM(Mu ltip le-InputMu ltip le-Output and O rthogonal Frequency D i-vision Mu ltip lexing)系统中的一种信道估计方法。该方法只需对重要抽头进行估计,且两步算法在导频数小于信道响应时间和未知功率延迟分布的情况下,可获得与经典的LMMSE(L inearM in imum M ean Square Error)算法相近的性能,计算机仿真结果证实了其有效性。

一种残余频偏算法在OFDM同步跟踪中的应用

在正交频分复用(OFDM)系统中,定时误差、多普勒效应以及本地振荡器不稳定等因素都会引起估计频率产生偏移,致使子载波星座相位发生旋转,并随着时间而不断累积,最终导致解调失效。为此,提出了用均方误差短时反馈算法来矫正旋转相位的新方法。该方法利用在反馈时间段内计算得出的残余相偏来直接矫正后续接收数据的相位。仿真结果表明,该算法很好地满足实时跟踪的要求,具有运算简单、速度快等特点,同时也提高了系统的性能。

Joint Transceiver Designs for Full-Duplex MIMO SWIPT Systems Based on MSE Criterion

For the simultaneous wireless information and power transfer(SWIPT), the full-duplex MIMO system can achieve simultaneous transmission of information and energy more efficiently than the half-duplex. Based on the mean-square-error(MSE) criterion, the optimization problem of joint transceiver design with transmitting power constraint and energy harvesting constraint is formulated. Next, by semidefinite relaxation(SDR) and randomization method, the SDRbased scheme is proposed. In order to reduce the complexity, the closed-form scheme is presented with some simplified measures. Robust beamforming is then studied considering the practical condition. The simulation results such as MSE versus signal-noise-ratio(SNR), MSE versus the iteration number, well prove the performance of the proposed schemes for the system model.

Joint Source and Relay Precoding/Beamforming Algorithm for OFDM Multi-user Cooperative Relay Systems

In order to reduce the feedback load of multi-user orthogonal frequency division multiplexing （ OFDM ） -based wireless systems, a practiral limited bits feedback precoding algorithm is proposed with direct source-destination link based on amplify-and- forward cooperative relay network under frequency selective fading channels. Using joint minimum mean square error（MMSE） filter, the receiving decoding matrix is designed for each user in the paper. Source precoding （beamforming） matrix is optimized with convex function of weight mean square error （MSE）. Relay precoding matrix is obtained under MSE decomposition and convex optimization. The precoding matrix index is fed back for clustered subcarrier of OFDM with limited feedback. Then using interpolation algorithm, all precoding matrices are achieved at base station （BS） and relay nodes. Simulations indicate the effectiveness of the proposed limited feedback joint precoding and beam_formlng design. The proposed method can improve bit error rate （BER） performance and obtain better sum-rate performance in contrast to existing algorithms. It displays the BER performance is close to that of the unquantified precoding feedback method.

Estimation of Population Variance Using the Coefficient of Kurtosis and Median of an Auxiliary Variable under Simple Random Sampling

In this study we have proposed a modified ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coefficient of kurtosis and median of an auxiliary variable x. The estimator’s properties have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some specified conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency. The developed estimator therefore is suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.

Improved estimator of the continuous-time kernel estimator

There have been many papers presenting kernel density estimators for a strictly stationary continuous time process observed over the time interval [0, T ]. However the estimators do not satisfy the property of mean-square continuity if the process is mean-square continuous. In this paper we present a modified kernel estimator and substantiate that the modified estimator satisfies the property of mean-square continuity. In a simulation study the results show the modified estimator is better than the original estimator in some cases.

Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation （ANC） system by using the least mean square （LMS） algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ＇C＇ code implementation of LMS algorithm is introduced and simulated in code composer studio （CCS）, then realized on the digital signal processor （DSP） C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio （SNR）. The experimentam results demonstrate that the designed system shows a consider- able improvement in SNR.

A Comparison of Four Methods of Estimating the Scale Parameter for the Exponential Distribution

In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.

基于KPCA-GA-BP模型的页岩气集输管道的内腐蚀速率预测

针对页岩气集输管道的内腐蚀,提出了一种基于KPCA-GA-BP组合模型的腐蚀速率预测算法。以某条页岩气集输管道的检测结果作为训练数据,运用反向传播(BP)神经网络建立预测模型,运用遗传算法(GA)优化了神经网络权值和阈值的初始值,运用核主成分分析法(KPCA)对数据进行了降维,在模型建立的过程中不断优化提升模型的预测精度,采用所建模型对另一条相邻管道进行预测并开挖验证。结果表明:选择TRAINGDM作为训练函数,隐含层节点为(8,1),遗传算法进化数为50,种群规模为100,交叉概率为0.3,变异概率为0.2,运用KPCA将数据从7维降为4维后,此模型的均方误差最低为0.12,当该模型用于相邻管道的预测时,均方误差为0.14。运用KPCAGA-BP模型,对页岩气集输管道内腐蚀速率进行预测具有一定的准确性,此模型可用于辅助指导现场内腐蚀直接评价等相关工作。

基于差分隐私的直方图发布方法综述

在数字经济时代,数据发布是数据共享的重要环节。直方图数据发布是数据发布的常见方式,但它面临着隐私泄露的问题。为此,对基于差分隐私(DP)的直方图数据发布方法进行了研究。首先,介绍了DP和直方图的相关性质,以及近5年国内外针对静态数据集和流数据的直方图发布方法的研究,并讨论了静态数据下直方图分组数、分组方式、噪声误差和分组误差的均衡,以及隐私预算分配问题。其次,探讨了动态数据下数据采样、数据预测以及滑动窗口实现分组的问题;同时针对面向区间树结构的DP直方图发布方法,将原始数据与树结构进行转化,并讨论了树结构数据的加噪、基于树结构的优化、树结构的隐私预算的分配等;此外,还讨论了直方图发布数据的可用性和隐私性、查询范围和查询精度问题。最后,通过对相关算法进行对比分析,总结了各算法的优缺点,以及部分算法的定量分析比较及适用场景,展望了未来基于DP的直方图在不同数据场景中的研究方向。

A Novel Approach for Developing a Linear Regression Model within Logistic Cluster Using Scikit-Learn

Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.

基于新阈值函数的小波阈值去噪算法

针对传统小波阈值函数在阈值处的不连续性、小波估计系数存在偏差等不足,导致去噪后的图像出现失真、产生吉布斯震荡等问题,提出了一种改进的阈值函数,与常用的硬阈值、软阈值以及已有改进的阈值函数相比,该函数不仅易于计算,而且具有优越的数学特性。为了验证该阈值函数的优越性,通过仿真实验对几种小波去噪方法的峰值信噪比(PSNR)与均方差(MSE)进行了对比。实验结果表明,此去噪方法无论是在视觉效果上,还是在均方差和信噪比性能分析上均优于常用的阈值函数。

室内可见光通信中的分数间隔均衡技术

研究了室内可见光通信系统分数间隔均衡技术,以降低码间干扰对该通信系统性能的影响。针对室内可见光通信系统的传输特点和信号调制特性,建立了室内可见光通信链路模型;在此基础上,提出了分数间隔均衡方法,并利用最小均方误差准则优化设计均衡器。最后,通过计算机仿真,对提出的分数间隔均衡方法进行了性能评估。实验结果表明,相同误码率条件下,T/2分数间隔均衡器比符号间隔均衡器的性能优1～2dB,能够更有效地抑制由信道多径效应等引起的码间干扰,提高信号传输的可靠性。

基于小波变换的电能质量监测

针对噪声系数的幅度随着尺度增加而减小的特点,提出了一种改进的基于小波分解层数的波段自适应降噪算法.利用Matlab仿真软件建立了电压暂降的系统模型,运用小波分析的方法对污染白噪声的电压暂降信号进行了时频分析和降噪处理,分析了软硬阈值降噪方法.实验结果表明,该方法通过调整两个可调参数获得较优的小波系数阈值估计,与常见的三种典型算法相比不仅较好地去除了噪声干扰,且信噪比更高,均方误差更小,同时具有更优的信息保全能力.