Closed-form MSE and achievable rate for indirect learning DPD

The digital pre-distortion (DPD) signal processing is an effective way to mitigate the power amplifier (PA) nonlinearity effect. For communication systems containing DPD and PA, it is difficult to acquire performance metrics closed-forms for any DPD architecture since there was no mathematical expression for each DPD coefficient. Usually, researchers look for more efficient DPD algorithms for DPD coefficients (compared to the existing ones) in terms of computational complexity, delay, power consumption, etc. Consequently, the performance is evaluated through intensive simulation. In this paper, we show how one can exploit the results of our recent work to mathematically model the indirect learning architecture (ILA) DPD and efficiently derive important measures in communication systems, e.g. normalized mean square error (NMSE), achievable rate, and signal-to-noise plus distortion ratio (SNDR). The author would like to clarify that this work might be the first one to provide closed-form analysis for DPD systems. We think the provided framework/analysis will open the door to other researchers/engineers to plug their own assumptions and derive the performance metrics. The derived expressions of the performance metrics (NMSE, SNDR, and achievable rate) are validated through Monte Carlo simulations. We also derive a closed-form expression for the achievable rate bound for the transmit chain. Moreover, we analytically study the effect of the thermal noise and the quantization noise, in the analog-digital conversion (ADC) process, on the NMSE and achievable rate. The analytical expressions are validated through numerical simulations.     

Tile-net for undersampled cardiovascular CINE magnetic resonance imaging

We propose the “Tile-net” method based on dividing an image into smaller tiles. Using the tile as the input to the neural network, the network is simplified substantially. The Tile-net learns at a much faster rate than the networks without tiling. The training and reconstruction times for the Tile-net are reduced by 40% and 33%, respectively compared to the networks without tiling. The Tile-net performance is evaluated through the normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structure similarity index measure (SSIM) and the quality of the reconstructed image for test datasets. The Tile-net does not degrade performance; however, it reduces the NMSE by 0.3% compared to the networks without tiling.     

Improving spectral efficiency via pilot assignment and subarray selection under realistic XL-MIMO channels

The main requirements for 5G and beyond connectivity include a uniform high quality of service, which can be attained in crowded scenarios by extra-large MIMO (XL-MIMO) systems. Another requirement is to support an increasing number of connected users in (over)crowded machine-type communication (mMTC). In such scenarios, pilot assignment (PA) becomes paramount to reduce pilot contamination and consequently improve spectral efficiency (SE). We propose a novel quasi-optimal low-complexity iterative pilot assignment strategy for XL-MIMO systems, based on a genetic algorithm (GA). The proposed GA-based PA procedure turns the quality of service more uniform, taking into account the normalized mean-square error (NMSE) of channel estimation from each candidate of the population. The simulations reveal that the proposed iterative procedure minimizes the channel estimation NMSE averaged over the UEs. The second procedure is the subarray (SA) selection. In XL-MIMO systems, commonly a UE is close to an SA antenna subset such that a sufficient data rate can be achieved if only a specific SA serves that UE. Thus, an SA selection procedure is investigated to make the system scalable by defining the maximum number of UEs each SA can help. Hence, the SA clusters are formatted based on the PA decision. Furthermore, we introduce an appropriate channel model for XL-MIMO, which considers a deterministic LoS component with a distance-dependent probability of existence combined with a stochastic spatially correlated Rayleigh NLoS fading component. The developed simulations and analyses rely on this suitable channel model under realistic assumptions of pilot contamination and correlated channels.     

Chaotic synchronization: a nonlinear predictive filtering approach

The synchronization of chaotic systems is a difficult task due to their sensitive dependence on the initial conditions. Perfect synchronization is almost impossible when noise is present in the system. One of the well known stochastic filtering algorithms that is used to synchronize chaotic systems in the presence of noise is the extended Kalman filter (EKF). However, for highly nonlinear systems, the approximation error introduced by the EKF has been shown to be relatively high. In this paper, a nonlinear predictive filter (NPF) is proposed for synchronizing chaotic systems. In this scheme, it is not required to approximate the underlying nonlinearity and hence there is no need to compute the Jacobian of the chaotic system. Numerical simulations are carried out to compare the performances of the NPF and EKF algorithms for synchronizing different sets of chaotic systems and/or maps. The well known Lorenz and Mackey-Glass systems as well as Ikeda map are used for numerical evaluation of the performance. Results clearly show that the NPF based approach is superior to the EKF based approach in terms of the normalized mean square error (NMSE), total NMSE, and the time taken for synchronization (measured in terms of the normalized instantaneous square error) for all the systems and/or maps considered.     

A method for calibrating the position error of phase retrieval with transverse translation diversity in optical wavefront measurement

We present a method for calibrating the position error of phase retrieval with transverse translation diversity in optical wavefront in situation where the position error sharply influences algorithm precision. This method involves testifying that the essence of the position error in phase retrieval is the translation in frequency domain and the minimum of iterative error against position error reaches when there is no position error. Then the least square method is used for fitting the relationship between the iterative error and the assumed position error in polynomial function. The computer simulations used to prove the validity of this method are described. The results indicated that this method can calibrate the position error to approximately 1/500 mm which nearly has no influence on the phase retrieval.     

水声通信低复杂度最大似然联合均衡译码方法

现有Turbo均衡采用迭代联合均衡译码结构,这一迭代过程在信噪比高于某一门限后才能产生正向的迭代增益,当信道码间干扰较为严重或者采用高阶调制方式时,该信噪比门限较高;同时,现有Turbo均衡的性能还受限于其交织器长度,故当码块长度较短时,其性能并不理想。针对以上问题,提出了一种基于Spinal码的联合均衡译码方法。首先,该方法采用非迭代的最大似然联合均衡译码结构,有效避免了Turbo均衡迭代结构所带来的信噪比门限高问题;其次,该方法采用信息混合能力更强的Spinal码,有助于提高码块长度受限时的误码率性能;最后,该方法利用Spinal码可以有效增加相似信息序列间汉明距离的特点,将联合均衡译码时间复杂度降低为仅随信息序列长度与信道响应长度呈线性增长,解决了本方法难以实际应用于实时水声通信的难题。在抚仙湖湖试中,同样实现速率为3.34kbps数据通信,该方法的误码率优于Turbo均衡方法两个数量级以上。时变水声信道下的仿真以及湖试结果表明,该方法在码长受限的情况下仍在复杂水声信道中表现出更好性能,其所需的最低输入信噪比显著低于现有Turbo均衡方法。      

Tunable-slip boundaries for coarse-grained simulations of fluid flow

On the micro- and nanoscale, classical hydrodynamic boundary conditions such as the no-slip condition no longer apply. Instead, the flow profiles exhibit "slip" at the surface, which is characterized by a finite slip length (partial slip). We present a new, systematic way of implementing partial-slip boundary conditions with arbitrary slip length in coarse-grained computer simulations. The main idea is to represent the complex microscopic interface structure by a spatially varying effective viscous force. An analytical equation for the resulting slip length can be derived for planar and for curved surfaces. The comparison with computer simulations of a DPD (dissipative particle dynamics) fluid shows that this expression is valid from full slip to no slip.     

A subpixel motion estimation algorithm based on digital correlation for illumination variant and noise image sequences

One of the challenges in practical subpixel motion estimation is how to obtain high accuracy with sufficient robustness to both illumination variations and additive noise. Motivated by the fact that the normalized spatial cross-correlation is invariant to illumination, we introduce a gradient-based subpixel registration method by maximizing the digital correlation (DC) function between the reference and target frames. Such DC function is remodeled with the presence of image noise, yielding that the correlation coefficient is only sensitive to noise standard variance. To fairly suppress the noise corruption, not only the target frame but also the reference one is reformulated into Taylor gradient expression with half but opposite motion vector. The final solution to motion estimates can be approximated into a closed form by reserving first-order coefficient terms of unregistered motion variables. The error trend of approximated solution is discussed. Computer simulations and actual experiments’ results demonstrate the superiority of the proposed method to the LMSE-based method and ordinary DC method when illumination variations and noise exist. Among the experiments, the influences of real subpixel translation value and noise variance degree on accuracy are studied; correspondingly, an optimized iterative idea for big translations and the recommended noise level adaptive to our method are introduced.     

Modeling the lossy transmission of correlated sources in multiple access fading channels

In this paper, we develop accurate distortion models for the lossy transmission of two correlated sources in a multiple access Rayleigh fading channel. We focus on a class of real-life communication systems, where the source and channel coders have already been designed separately and can only be configured during the system operation. We investigate three different source coding schemes: distributed source coding (DSC), layered source coding, and independent compression through quantization. With the later scheme the sources are jointly decoded with minimum mean square error (MMSE) estimation at the receiver. We also consider two different transmission schemes: Orthogonal transmissions and interfering transmissions decoded with a successive interference cancellation (SIC) decoder. Our final closed-form analytical models are used to determine the optimal combination of source coding and transmission schemes, as well as their optimal configuration. Hence, we exercise joint source and channel coding (JSCC) by optimizing the system configuration. Through simulations, we first validate the analytical model and illustrate the performance of different schemes. Finally, we demonstrate the JSCC gains achieved by our system.     

基于BP神经网络的图像质量评价参数优化

 在基于噪声图像的无参考峰值信噪比质量评价方法中,为了得到最优的阈值参数,提出以图像块均方误差阈值threshold1、噪声检测阈值threshold2为输入因子, 以Pearson相关系数和Spearman等级相关系数为输出因子, 以实验值为样本建立［2 7 2］单隐层BP神经网络模型,应用BP神经网络的泛化能力实现对相关阈值参数的预测优化,为阈值参数的选择提供理论依据。实验结果表明,所建立的数学模型可靠,预测结果与试验值的偏差小,训练好的BP神经网络能够比较准确地预测不同阈值参数下的相关系数。优化后,选取threshold1=101,threshold2=4,Pearson相关系数达到了-0.895 0,Spearman等级相关系数达到了-0.913 6,评价效果得到提高,且节省大量时间。     

地层渗透参数反演计算

利用渗流源网格压力(及井底压力)对网格渗透率导数关系(称为渗透率压力场敏感系数)实现由三维渗流方程压强数值解计算井底压力对网格渗透参数导数的计算.基于渗透率压力场敏感系数的计算结果构造地层渗透参数反演方程,由多井系统井压监测数据实现反演地层渗透参数的计算.算例显示:反演方法对均匀(或分块均匀)油藏模型的计算取得了较好的结果,反演迭代10次后计算结果与模型参数的最大相对误差收敛于1%之内.该方法对油藏动态模拟研究及实现油藏参数动态跟踪具有实际意义.     

前后向子分段相位差频率估计法

为提高直接频率估计法的精度并推导出估计误差方差的闭合表达式,本文提出基于前后向子分段相位差的直接频率估计法. 该方法对样本的前向和后向两个子段做快速傅里叶变换,再提取这两个子段变换后的峰值谱相位差而获得频率估计. 本文证明了该估计器具有无偏性,并推导出其频率估计方差的闭合理论表达式. 仿真实验验证了该闭合表达式的正确性,故本文方法具有更高的测频精度和广泛的应用前景. 关键词： 频率估计 前后向子段 相位差 克拉美罗限     

Nonlinear fluctuation,frequency and stability analyses in free vibration of circular sector oscillation systems

In the present paper, the modified homotopy perturbation method (MHPM) is employed to investigate about both nonlinear swinging oscillation and the stability of circular sector oscillation systems. The sensitivity study performed for frequency analysis of the mentioned oscillatory circular sector body shows that frequency of nonlinear oscillation depends on some specific parameters and can be optimized. Furthermore onset of the instability is dependent to angle α and initial amplitude.Comparisons made among the results of the present closed-form analytical solution and the traditional numerical iterative time integration solution confirms the accuracy and efficiency of the presented analytical solution.In contrast to the available numerical methods, the present analytical method is free from the numerical damping and the time integration accumulated errors. Moreover, in comparison with the traditional multistep numerical iterative time integration methods, a much less computational time is required for the present analytical method. Responses of the dynamical systems to some extent are affected by the natural frequencies. Results reveal that for nonlinear systems, the natural frequency is remarkably affected by the initial conditions.     

Evaluation of the generalized Goodwin-Staton integral using binomial expansion theorem

A new analytical technique for evaluating the generalized Goodwin-Staton integral (GGS) is described. A closed-form evaluation is presented. The GGS integral are expressed in terms of linear combinations of binomial coefficients and incomplete gamma function. A further comparison of analytical results with numerical models demonstrates a high accuracy of the developed analytical solution. The convergence of the results is shown.     

Statistical Physics Analysis of Maximum a Posteriori Estimation for Multi-channel Hidden Markov Models

The performance of Maximum a posteriori (MAP) estimation is studied analytically for binary symmetric multi-channel Hidden Markov processes. We reduce the estimation problem to a 1D Ising spin model and define order parameters that correspond to different characteristics of the MAP-estimated sequence. The solution to the MAP estimation problem has different operational regimes separated by first order phase transitions. The transition points for L-channel system with identical noise levels, are uniquely determined by L being odd or even, irrespective of the actual number of channels. We demonstrate that for lower noise intensities, the number of solutions is uniquely determined for odd L, whereas for even L there are exponentially many solutions. We also develop a semi analytical approach to calculate the estimation error without resorting to brute force simulations. Finally, we examine the tradeoff between a system with single low-noise channel and one with multiple noisy channels.     

应用数字傅里叶滤波方法提高近红外光谱多元校正模型稳健性的研究

在近红外光谱多元校正方法实际应用中,经常遇到这样的情况,近红外光谱校正模型仅适用于建模时的测量条件,而在测量条件稍有变化时就无法实现样品的准确预测。文章主要研究采用数字傅里叶滤波预处理方法提高近红外光谱多元校正模型稳健性。文章将数字傅里叶滤波预处理方法应用于葡萄糖水溶液的温度实验,实验1和实验2分别在恒温25 ℃和恒温30 ℃进行光谱测量;实验3在未控温的室内环境下进行光谱测量。采用实验1和实验2的样品作为训练集进行模型训练和优化,模型建立完毕之后,采用实验3的样品作为验证集进行模型预测能力评价。结果表明,如果训练集样品未经过预处理而直接建立偏最小二乘(PLS)多元校正模型,则验证集样品均方根预测误差(RMSEP)为664.47 mg·dL-1。而训练集和验证集样品经过傅里叶滤波预处理之后分别进行PLS建模和预测,验证集样品均方根预测误差(RMSEP)降低为58.43 mg·dL-1,样品预测值与参考值的相关性也得到提高。可见,采用数字傅里叶滤波预处理方法可以提高多元校正模型的稳健性。     

基于块稀疏学习的光谱基线校正方法

光谱数据在采集过程中易发生基线偏移现象,导致后续的鉴别和分析结果偏离真实值。因此,在光谱数据分析前,需利用基线校正技术获取更为准确的光谱数据。基于稀疏贝叶斯学习（SBL）的基线校正方法无需人工选择参数,基线校正结果在贝叶斯框架下具有最优性。然而,现有的稀疏贝叶斯建模较为简单,无法适用于复杂的稀疏结构。在实际应用中,当纯谱的某些谱峰较宽时,对应的稀疏向量将具有一定的块稀疏特性。利用额外的块稀疏结构,有助于进一步提升SBL方法的性能。为了建模稀疏向量的块稀疏结构特性,在原有的贝叶斯模型框架中引入模式耦合分层模型。得益于稀疏贝叶斯框架固有的学习能力,引入的模式耦合分层模型可自适应地学习稀疏向量的块稀疏结构,从而大幅提升了基于SBL的基线校正方法的性能。为验证本文方法的基线校正性能,首先利用模拟数据集进行仿真实验,并将该方法与SSFBCSP方法和SBL-BC方法在不同噪声方差条件下进行对比。仿真实验结果表明,该方法恢复谱峰较宽纯谱的效果提升明显,特别是当噪声方差较大时,其他方法的性能均有不同程度的下降,但该方法依然具有较好的稳定性。蒙特卡罗仿真实验结果也显示该方法纯谱拟合的标准化均方根误差明显优于其他对比方法。最后,利用色谱数据集与三种矿物的拉曼光谱数据集进行实测数据的基线校正性能验证,结果表明该方法能产生比其他方法更为平滑的纯谱拟合结果,且去噪效果更优。     

再入过载的匹配渐进展开分析与卸载方法研究

探月飞船返回地球时将以第二宇宙速度再入地球大气层,面临极其严苛的气动环境,因此对于再入气动过载的分析具有重要意义.再入运动方程是一组非线性很强的常微分方程,数值方法计算量大,不适用于在线任务.因此,本文采用一种近似解法对气动过载进行分析.首先,基于匹配渐进展开方法将再入纵向运动解在大气外层区域与内层区域分别展开,得到统一形式的闭型近似解,在此基础上分段求解气动过载,并与精确解进行对比分析.其次,利用闭型近似解,通过当前状态反解虚拟初始条件,在此基础上提出初次过载峰值的解析预测方法,并分析了不同条件下预测的相对误差变化规律.最后,基于过载峰值的解析预测对飞船的初次再入过程进行卸载,将飞船在再入过程中耗散的总能量进行重新分配,并通过蒙特卡罗飞行仿真试验验证了卸载方法的有效性.     

噪声模态单元预判的经验模态分解脉冲星信号消噪

为了改善脉冲星辐射脉冲信号的消噪效果, 提出了一种基于噪声模态单元预判的经验模态分解(EMD) 消噪声方法. 该方法首先利用EMD将含噪辐射脉冲信号分解为一组内蕴模态函数(IMF), 根据IMF系数的统计特性采用局部均方误差准则进行噪声模态单元预判, 并将噪声模态单元置零; 然后对噪声模态单元预判处理后的IMF以模态单元为基本单位进行最优比例萎缩消噪, 从而达到抑制噪声、保留信号的目的. 实验结果表明: 与Sure Shrink小波阈值法、Bayes Shrink小波阈值法和EMD模态单元比例萎缩法相比, 基于噪声模态单元预判的EMD消噪方法可以更有效地去除脉冲辐射信号中的噪声, 同时更好地保留信号突变处的细节信息特征, 在信噪比、 均方误差、峰值相对误差、峰位误差和相位误差等方面都有一定程度的改善. 关键词： 脉冲星信号消噪 经验模态分解 噪声模态单元预判 局部均方误差